Warning: Permanently added '2620:52:3:1:dead:beef:cafe:c24b' (ED25519) to the list of known hosts. You can reproduce this build on your computer by running: sudo dnf install copr-rpmbuild /usr/bin/copr-rpmbuild --verbose --drop-resultdir --task-url https://copr.fedorainfracloud.org/backend/get-build-task/9886669-fedora-43-ppc64le --chroot fedora-43-ppc64le Version: 1.6 PID: 2652 Logging PID: 2654 Task: {'allow_user_ssh': False, 'appstream': False, 'background': True, 'build_id': 9886669, 'buildroot_pkgs': [], 'chroot': 'fedora-43-ppc64le', 'enable_net': False, 'fedora_review': False, 'git_hash': '30d15ba4f7969b4deeaff31b36005d8405722bcc', 'git_repo': 'https://copr-dist-git.fedorainfracloud.org/git/psimovec/scipy-1.10.0/python-scikit-learn', 'isolation': 'default', 'memory_reqs': 2048, 'package_name': 'python-scikit-learn', 'package_version': '1.8.0~rc1-1', 'project_dirname': 'scipy-1.10.0', 'project_name': 'scipy-1.10.0', 'project_owner': 'psimovec', 'repo_priority': None, 'repos': [{'baseurl': 'https://download.copr.fedorainfracloud.org/results/psimovec/scipy-1.10.0/fedora-43-ppc64le/', 'id': 'copr_base', 'name': 'Copr repository', 'priority': None}, {'baseurl': 'http://kojipkgs.fedoraproject.org/repos/rawhide/latest/$basearch/', 'id': 'http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch', 'name': 'Additional repo http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch'}], 'sandbox': 'psimovec/scipy-1.10.0--https://src.fedoraproject.org/user/cstratak', 'source_json': {}, 'source_type': None, 'ssh_public_keys': None, 'storage': None, 'submitter': 'https://src.fedoraproject.org/user/cstratak', 'tags': [], 'task_id': '9886669-fedora-43-ppc64le', 'timeout': 18000, 'uses_devel_repo': False, 'with_opts': [], 'without_opts': []} Running: git clone https://copr-dist-git.fedorainfracloud.org/git/psimovec/scipy-1.10.0/python-scikit-learn /var/lib/copr-rpmbuild/workspace/workdir-rh2752_3/python-scikit-learn --depth 500 --no-single-branch --recursive cmd: ['git', 'clone', 'https://copr-dist-git.fedorainfracloud.org/git/psimovec/scipy-1.10.0/python-scikit-learn', '/var/lib/copr-rpmbuild/workspace/workdir-rh2752_3/python-scikit-learn', '--depth', '500', '--no-single-branch', '--recursive'] cwd: . rc: 0 stdout: stderr: Cloning into '/var/lib/copr-rpmbuild/workspace/workdir-rh2752_3/python-scikit-learn'... Running: git checkout 30d15ba4f7969b4deeaff31b36005d8405722bcc -- cmd: ['git', 'checkout', '30d15ba4f7969b4deeaff31b36005d8405722bcc', '--'] cwd: /var/lib/copr-rpmbuild/workspace/workdir-rh2752_3/python-scikit-learn rc: 0 stdout: stderr: Note: switching to '30d15ba4f7969b4deeaff31b36005d8405722bcc'. You are in 'detached HEAD' state. You can look around, make experimental changes and commit them, and you can discard any commits you make in this state without impacting any branches by switching back to a branch. If you want to create a new branch to retain commits you create, you may do so (now or later) by using -c with the switch command. Example: git switch -c Or undo this operation with: git switch - Turn off this advice by setting config variable advice.detachedHead to false HEAD is now at 30d15ba automatic import of python-scikit-learn Running: dist-git-client sources cmd: ['dist-git-client', 'sources'] cwd: /var/lib/copr-rpmbuild/workspace/workdir-rh2752_3/python-scikit-learn rc: 0 stdout: stderr: INFO: Reading stdout from command: git rev-parse --abbrev-ref HEAD INFO: Reading stdout from command: git rev-parse HEAD INFO: Reading sources specification file: sources INFO: Downloading scikit_learn-1.8.0rc1.tar.gz INFO: Reading stdout from command: curl --help all INFO: Calling: curl -H Pragma: -o scikit_learn-1.8.0rc1.tar.gz --location --connect-timeout 60 --retry 3 --retry-delay 10 --remote-time --show-error --fail --retry-all-errors https://copr-dist-git.fedorainfracloud.org/repo/pkgs/psimovec/scipy-1.10.0/python-scikit-learn/scikit_learn-1.8.0rc1.tar.gz/md5/7ca3b122e8c50a801c579c27484f06c7/scikit_learn-1.8.0rc1.tar.gz % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 7170k 100 7170k 0 0 10.8M 0 --:--:-- --:--:-- --:--:-- 10.8M INFO: Reading stdout from command: md5sum scikit_learn-1.8.0rc1.tar.gz tail: /var/lib/copr-rpmbuild/main.log: file truncated Running (timeout=18000): unbuffer mock --spec /var/lib/copr-rpmbuild/workspace/workdir-rh2752_3/python-scikit-learn/python-scikit-learn.spec --sources /var/lib/copr-rpmbuild/workspace/workdir-rh2752_3/python-scikit-learn --resultdir /var/lib/copr-rpmbuild/results --uniqueext 1765205672.975907 -r /var/lib/copr-rpmbuild/results/configs/child.cfg INFO: mock.py version 6.5 starting (python version = 3.13.7, NVR = mock-6.5-1.fc42), args: /usr/libexec/mock/mock --spec /var/lib/copr-rpmbuild/workspace/workdir-rh2752_3/python-scikit-learn/python-scikit-learn.spec --sources /var/lib/copr-rpmbuild/workspace/workdir-rh2752_3/python-scikit-learn --resultdir /var/lib/copr-rpmbuild/results --uniqueext 1765205672.975907 -r /var/lib/copr-rpmbuild/results/configs/child.cfg Start(bootstrap): init plugins INFO: tmpfs initialized INFO: selinux enabled INFO: chroot_scan: initialized INFO: compress_logs: initialized Finish(bootstrap): init plugins Start: init plugins INFO: tmpfs initialized INFO: selinux enabled INFO: chroot_scan: initialized INFO: compress_logs: initialized Finish: init plugins INFO: Signal handler active Start: run INFO: Start(/var/lib/copr-rpmbuild/workspace/workdir-rh2752_3/python-scikit-learn/python-scikit-learn.spec) Config(fedora-43-ppc64le) Start: clean chroot Finish: clean chroot Mock Version: 6.5 INFO: Mock Version: 6.5 Start(bootstrap): chroot init INFO: mounting tmpfs at /var/lib/mock/fedora-43-ppc64le-bootstrap-1765205672.975907/root. INFO: calling preinit hooks INFO: enabled root cache INFO: enabled package manager cache Start(bootstrap): cleaning package manager metadata Finish(bootstrap): cleaning package manager metadata INFO: Guessed host environment type: unknown INFO: Using container image: registry.fedoraproject.org/fedora:43 INFO: Pulling image: registry.fedoraproject.org/fedora:43 INFO: Tagging container image as mock-bootstrap-190f79c2-eb3f-47a9-bb68-8810ede4372f INFO: Checking that e65dbd071db175394623ac211471e091ef8fcf94251d5c60550dbe55104e0508 image matches host's architecture INFO: Copy content of container e65dbd071db175394623ac211471e091ef8fcf94251d5c60550dbe55104e0508 to /var/lib/mock/fedora-43-ppc64le-bootstrap-1765205672.975907/root INFO: mounting e65dbd071db175394623ac211471e091ef8fcf94251d5c60550dbe55104e0508 with podman image mount INFO: image e65dbd071db175394623ac211471e091ef8fcf94251d5c60550dbe55104e0508 as /var/lib/containers/storage/overlay/41e6cbfccd6fb3c7d2cef0ec2e7136254fa164e4b6c376954c29032807363464/merged INFO: umounting image e65dbd071db175394623ac211471e091ef8fcf94251d5c60550dbe55104e0508 (/var/lib/containers/storage/overlay/41e6cbfccd6fb3c7d2cef0ec2e7136254fa164e4b6c376954c29032807363464/merged) with podman image umount INFO: Removing image mock-bootstrap-190f79c2-eb3f-47a9-bb68-8810ede4372f INFO: Package manager dnf5 detected and used (fallback) INFO: Not updating bootstrap chroot, bootstrap_image_ready=True Start(bootstrap): creating root cache Finish(bootstrap): creating root cache Finish(bootstrap): chroot init Start: chroot init INFO: mounting tmpfs at /var/lib/mock/fedora-43-ppc64le-1765205672.975907/root. INFO: calling preinit hooks INFO: enabled root cache INFO: enabled package manager cache Start: cleaning package manager metadata Finish: cleaning package manager metadata INFO: enabled HW Info plugin INFO: Package manager dnf5 detected and used (direct choice) INFO: Buildroot is handled by package management downloaded with a bootstrap image: rpm-6.0.0-1.fc43.ppc64le rpm-sequoia-1.9.0-2.fc43.ppc64le dnf5-5.2.17.0-2.fc43.ppc64le dnf5-plugins-5.2.17.0-2.fc43.ppc64le Start: installing minimal buildroot with dnf5 Updating and loading repositories: Copr repository 100% | 417.0 KiB/s | 369.4 KiB | 00m01s updates 100% | 5.0 MiB/s | 14.6 MiB | 00m03s fedora 100% | 8.5 MiB/s | 33.5 MiB | 00m04s Additional repo http_kojipkgs_fedorapr 100% | 3.4 MiB/s | 14.0 MiB | 00m04s Repositories loaded. 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http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 208.7 KiB publicsuffix-list-dafsa noarch 20250616-2.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 69.1 KiB pyproject-srpm-macros noarch 1.18.6-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.9 KiB python-srpm-macros noarch 3.14-9.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 51.6 KiB qt5-srpm-macros noarch 5.15.18-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 500.0 B qt6-srpm-macros noarch 6.10.1-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 464.0 B readline ppc64le 8.3-2.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 627.5 KiB rpm ppc64le 6.0.0-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 4.2 MiB rpm-build-libs ppc64le 6.0.0-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 328.0 KiB rpm-libs ppc64le 6.0.0-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.2 MiB rpm-plugin-selinux ppc64le 6.0.0-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 67.9 KiB rpm-sequoia ppc64le 1.9.0-2.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 4.9 MiB rpm-sign-libs ppc64le 6.0.0-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 67.6 KiB rust-srpm-macros noarch 28.2-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 5.5 KiB selinux-policy noarch 42.19-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 32.0 KiB selinux-policy-targeted noarch 42.19-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 18.7 MiB setup noarch 2.15.0-27.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 724.9 KiB sqlite-libs ppc64le 3.51.0-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.9 MiB systemd-libs ppc64le 259~rc2-2.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 3.0 MiB systemd-standalone-sysusers ppc64le 259~rc2-2.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 393.7 KiB tpm2-tss ppc64le 4.1.3-8.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 2.5 MiB tree-sitter-srpm-macros noarch 0.4.2-1.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 8.3 KiB util-linux-core ppc64le 2.41.2-9.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 2.5 MiB xxhash-libs ppc64le 0.8.3-3.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 85.6 KiB xz-libs ppc64le 1:5.8.1-4.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 265.3 KiB zig-srpm-macros noarch 1-5.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.1 KiB zip ppc64le 3.0-44.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 889.8 KiB zlib-ng-compat ppc64le 2.3.2-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 197.4 KiB zstd ppc64le 1.5.7-3.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 573.9 KiB Installing groups: Buildsystem building group Transaction Summary: Installing: 178 packages Total size of inbound packages is 71 MiB. Need to download 71 MiB. After this operation, 277 MiB extra will be used (install 277 MiB, remove 0 B). [ 1/178] bzip2-0:1.0.8-21.fc43.ppc64le 100% | 217.7 KiB/s | 52.9 KiB | 00m00s [ 2/178] cpio-0:2.15-6.fc43.ppc64le 100% | 2.2 MiB/s | 293.8 KiB | 00m00s [ 3/178] coreutils-0:9.9-1.fc44.ppc64l 100% | 2.6 MiB/s | 1.2 MiB | 00m00s [ 4/178] diffutils-0:3.12-3.fc43.ppc64 100% | 3.8 MiB/s | 396.4 KiB | 00m00s [ 5/178] bash-0:5.3.0-2.fc43.ppc64le 100% | 3.7 MiB/s | 1.9 MiB | 00m01s [ 6/178] fedora-release-common-0:44-0. 100% | 481.0 KiB/s | 24.5 KiB | 00m00s [ 7/178] findutils-1:4.10.0-6.fc43.ppc 100% | 7.2 MiB/s | 578.5 KiB | 00m00s [ 8/178] glibc-minimal-langpack-0:2.42 100% | 1.0 MiB/s | 66.9 KiB | 00m00s [ 9/178] grep-0:3.12-2.fc43.ppc64le 100% | 5.0 MiB/s | 299.2 KiB | 00m00s [ 10/178] gzip-0:1.14-1.fc44.ppc64le 100% | 2.9 MiB/s | 172.6 KiB | 00m00s [ 11/178] gawk-0:5.3.2-2.fc43.ppc64le 100% | 6.1 MiB/s | 1.2 MiB | 00m00s [ 12/178] info-0:7.2-7.fc44.ppc64le 100% | 2.0 MiB/s | 203.1 KiB | 00m00s [ 13/178] patch-0:2.8-2.fc43.ppc64le 100% | 1.1 MiB/s | 123.9 KiB | 00m00s [ 14/178] redhat-rpm-config-0:343-16.fc 100% | 1.2 MiB/s | 73.0 KiB | 00m00s [ 15/178] rpm-build-0:6.0.0-1.fc44.ppc6 100% | 2.4 MiB/s | 152.5 KiB | 00m00s [ 16/178] sed-0:4.9-6.fc44.ppc64le 100% | 4.4 MiB/s | 314.3 KiB | 00m00s [ 17/178] tar-2:1.35-6.fc43.ppc64le 100% | 9.2 MiB/s | 875.3 KiB | 00m00s [ 18/178] shadow-utils-2:4.18.0-7.fc44. 100% | 10.5 MiB/s | 1.2 MiB | 00m00s [ 19/178] unzip-0:6.0-68.fc44.ppc64le 100% | 2.7 MiB/s | 192.3 KiB | 00m00s [ 20/178] which-0:2.23-3.fc43.ppc64le 100% | 877.6 KiB/s | 43.0 KiB | 00m00s [ 21/178] xz-1:5.8.1-4.fc44.ppc64le 100% | 7.1 MiB/s | 561.1 KiB | 00m00s [ 22/178] util-linux-0:2.41.2-9.fc44.pp 100% | 11.4 MiB/s | 1.2 MiB | 00m00s [ 23/178] fedora-repos-0:44-0.1.noarch 100% | 148.7 KiB/s | 9.1 KiB | 00m00s [ 24/178] coreutils-common-0:9.9-1.fc44 100% | 12.4 MiB/s | 2.1 MiB | 00m00s [ 25/178] glibc-0:2.42.9000-14.fc44.ppc 100% | 18.5 MiB/s | 3.2 MiB | 00m00s [ 26/178] glibc-common-0:2.42.9000-14.f 100% | 2.9 MiB/s | 362.5 KiB | 00m00s [ 27/178] rpm-0:6.0.0-1.fc44.ppc64le 100% | 6.4 MiB/s | 547.5 KiB | 00m00s [ 28/178] libblkid-0:2.41.2-9.fc44.ppc6 100% | 2.0 MiB/s | 142.4 KiB | 00m00s [ 29/178] libfdisk-0:2.41.2-9.fc44.ppc6 100% | 2.5 MiB/s | 177.3 KiB | 00m00s [ 30/178] liblastlog2-0:2.41.2-9.fc44.p 100% | 321.4 KiB/s | 23.8 KiB | 00m00s [ 31/178] libsmartcols-0:2.41.2-9.fc44. 100% | 1.4 MiB/s | 110.4 KiB | 00m00s [ 32/178] libmount-0:2.41.2-9.fc44.ppc6 100% | 2.3 MiB/s | 186.8 KiB | 00m00s [ 33/178] libuuid-0:2.41.2-9.fc44.ppc64 100% | 380.2 KiB/s | 27.4 KiB | 00m00s [ 34/178] util-linux-core-0:2.41.2-9.fc 100% | 6.7 MiB/s | 575.4 KiB | 00m00s [ 35/178] xz-libs-1:5.8.1-4.fc44.ppc64l 100% | 1.3 MiB/s | 126.5 KiB | 00m00s [ 36/178] fedora-gpg-keys-0:44-0.1.noar 100% | 1.4 MiB/s | 127.7 KiB | 00m00s [ 37/178] fedora-repos-rawhide-0:44-0.1 100% | 118.5 KiB/s | 8.6 KiB | 00m00s [ 38/178] rpm-libs-0:6.0.0-1.fc44.ppc64 100% | 6.7 MiB/s | 444.4 KiB | 00m00s [ 39/178] rpm-build-libs-0:6.0.0-1.fc44 100% | 2.2 MiB/s | 134.3 KiB | 00m00s [ 40/178] glibc-gconv-extra-0:2.42.9000 100% | 12.4 MiB/s | 1.6 MiB | 00m00s [ 41/178] rpm-sign-libs-0:6.0.0-1.fc44. 100% | 553.1 KiB/s | 28.8 KiB | 00m00s [ 42/178] libgcc-0:15.2.1-4.fc44.ppc64l 100% | 2.1 MiB/s | 116.9 KiB | 00m00s [ 43/178] libxcrypt-0:4.5.2-1.fc44.ppc6 100% | 2.0 MiB/s | 139.5 KiB | 00m00s [ 44/178] systemd-libs-0:259~rc2-2.fc44 100% | 10.7 MiB/s | 884.1 KiB | 00m00s [ 45/178] filesystem-0:3.18-50.fc43.ppc 100% | 20.3 MiB/s | 1.3 MiB | 00m00s [ 46/178] libselinux-0:3.9-5.fc44.ppc64 100% | 2.1 MiB/s | 112.5 KiB | 00m00s [ 47/178] ncurses-libs-0:6.5-7.20250614 100% | 7.6 MiB/s | 383.4 KiB | 00m00s [ 48/178] audit-libs-0:4.1.2-2.fc44.ppc 100% | 3.7 MiB/s | 155.1 KiB | 00m00s [ 49/178] libcap-ng-0:0.8.5-8.fc44.ppc6 100% | 696.3 KiB/s | 33.4 KiB | 00m00s [ 50/178] librtas-0:2.0.6-5.fc44.ppc64l 100% | 1.3 MiB/s | 83.2 KiB | 00m00s [ 51/178] pam-libs-0:1.7.1-3.fc43.ppc64 100% | 1.0 MiB/s | 63.6 KiB | 00m00s [ 52/178] readline-0:8.3-2.fc43.ppc64le 100% | 3.3 MiB/s | 243.7 KiB | 00m00s [ 53/178] bzip2-libs-0:1.0.8-21.fc43.pp 100% | 694.2 KiB/s | 49.3 KiB | 00m00s [ 54/178] zlib-ng-compat-0:2.3.2-1.fc44 100% | 1.2 MiB/s | 90.7 KiB | 00m00s [ 55/178] libacl-0:2.3.2-4.fc43.ppc64le 100% | 408.2 KiB/s | 26.5 KiB | 00m00s [ 56/178] libsemanage-0:3.9-4.fc44.ppc6 100% | 3.3 MiB/s | 135.2 KiB | 00m00s [ 57/178] libeconf-0:0.7.9-2.fc43.ppc64 100% | 839.1 KiB/s | 40.3 KiB | 00m00s [ 58/178] setup-0:2.15.0-27.fc44.noarch 100% | 3.5 MiB/s | 151.3 KiB | 00m00s [ 59/178] libstdc++-0:15.2.1-4.fc44.ppc 100% | 19.9 MiB/s | 1.0 MiB | 00m00s [ 60/178] gnupg2-0:2.4.8-4.fc43.ppc64le 100% | 21.4 MiB/s | 1.7 MiB | 00m00s [ 61/178] ima-evm-utils-libs-0:1.6.2-7. 100% | 495.7 KiB/s | 30.2 KiB | 00m00s [ 62/178] libfsverity-0:1.6-3.fc43.ppc6 100% | 424.9 KiB/s | 19.1 KiB | 00m00s [ 63/178] popt-0:1.19-9.fc43.ppc64le 100% | 1.1 MiB/s | 63.7 KiB | 00m00s [ 64/178] lua-libs-0:5.4.8-4.fc44.ppc64 100% | 2.6 MiB/s | 149.5 KiB | 00m00s [ 65/178] libcap-0:2.77-1.fc44.ppc64le 100% | 1.3 MiB/s | 94.7 KiB | 00m00s [ 66/178] libzstd-0:1.5.7-3.fc44.ppc64l 100% | 4.8 MiB/s | 408.1 KiB | 00m00s [ 67/178] sqlite-libs-0:3.51.0-1.fc44.p 100% | 8.2 MiB/s | 877.0 KiB | 00m00s [ 68/178] rpm-sequoia-0:1.9.0-2.fc43.pp 100% | 11.1 MiB/s | 1.6 MiB | 00m00s [ 69/178] elfutils-libelf-0:0.194-1.fc4 100% | 2.0 MiB/s | 212.0 KiB | 00m00s [ 70/178] elfutils-libs-0:0.194-1.fc44. 100% | 5.1 MiB/s | 311.2 KiB | 00m00s [ 71/178] elfutils-0:0.194-1.fc44.ppc64 100% | 6.7 MiB/s | 567.4 KiB | 00m00s [ 72/178] elfutils-debuginfod-client-0: 100% | 910.6 KiB/s | 50.1 KiB | 00m00s [ 73/178] libgomp-0:15.2.1-4.fc44.ppc64 100% | 5.6 MiB/s | 391.6 KiB | 00m00s [ 74/178] file-0:5.46-8.fc44.ppc64le 100% | 1.0 MiB/s | 49.7 KiB | 00m00s [ 75/178] file-libs-0:5.46-8.fc44.ppc64 100% | 11.2 MiB/s | 862.1 KiB | 00m00s [ 76/178] debugedit-0:5.2-3.fc44.ppc64l 100% | 1.8 MiB/s | 89.4 KiB | 00m00s [ 77/178] libarchive-0:3.8.3-1.fc44.ppc 100% | 5.9 MiB/s | 495.1 KiB | 00m00s [ 78/178] pkgconf-pkg-config-0:2.3.0-3. 100% | 186.9 KiB/s | 9.5 KiB | 00m00s [ 79/178] zstd-0:1.5.7-3.fc44.ppc64le 100% | 3.8 MiB/s | 194.2 KiB | 00m00s [ 80/178] curl-0:8.17.0-4.fc44.ppc64le 100% | 5.1 MiB/s | 242.0 KiB | 00m00s [ 81/178] binutils-0:2.45.50-9.fc44.ppc 100% | 27.8 MiB/s | 6.6 MiB | 00m00s [ 82/178] gpgverify-0:2.2-3.fc43.noarch 100% | 168.2 KiB/s | 11.1 KiB | 00m00s [ 83/178] pyproject-srpm-macros-0:1.18. 100% | 233.6 KiB/s | 13.3 KiB | 00m00s [ 84/178] qt5-srpm-macros-0:5.15.18-1.f 100% | 168.7 KiB/s | 8.6 KiB | 00m00s [ 85/178] qt6-srpm-macros-0:6.10.1-1.fc 100% | 183.5 KiB/s | 9.4 KiB | 00m00s [ 86/178] rust-srpm-macros-0:28.2-1.fc4 100% | 209.9 KiB/s | 10.7 KiB | 00m00s [ 87/178] ansible-srpm-macros-0:1-18.1. 100% | 497.7 KiB/s | 19.9 KiB | 00m00s [ 88/178] build-reproducibility-srpm-ma 100% | 306.1 KiB/s | 12.9 KiB | 00m00s [ 89/178] add-determinism-0:0.7.2-2.fc4 100% | 10.2 MiB/s | 926.2 KiB | 00m00s [ 90/178] linkdupes-0:0.7.2-2.fc44.ppc6 100% | 5.3 MiB/s | 388.8 KiB | 00m00s [ 91/178] dwz-0:0.16-2.fc43.ppc64le 100% | 1.4 MiB/s | 145.5 KiB | 00m00s [ 92/178] efi-srpm-macros-0:6-5.fc44.no 100% | 237.1 KiB/s | 22.5 KiB | 00m00s [ 93/178] filesystem-srpm-macros-0:3.18 100% | 314.4 KiB/s | 26.4 KiB | 00m00s [ 94/178] fonts-srpm-macros-1:5.0.0-1.f 100% | 363.9 KiB/s | 27.3 KiB | 00m00s [ 95/178] forge-srpm-macros-0:0.4.0-3.f 100% | 257.5 KiB/s | 20.1 KiB | 00m00s [ 96/178] fpc-srpm-macros-0:1.3-15.fc43 100% | 116.1 KiB/s | 7.9 KiB | 00m00s [ 97/178] gap-srpm-macros-0:2-1.fc44.no 100% | 133.1 KiB/s | 9.1 KiB | 00m00s [ 98/178] ghc-srpm-macros-0:1.9.2-3.fc4 100% | 182.2 KiB/s | 8.7 KiB | 00m00s [ 99/178] gnat-srpm-macros-0:6-8.fc43.n 100% | 148.9 KiB/s | 8.5 KiB | 00m00s [100/178] go-srpm-macros-0:3.8.0-1.fc44 100% | 629.1 KiB/s | 28.3 KiB | 00m00s [101/178] java-srpm-macros-0:1-7.fc43.n 100% | 147.1 KiB/s | 7.9 KiB | 00m00s [102/178] kernel-srpm-macros-0:1.0-27.f 100% | 105.0 KiB/s | 8.9 KiB | 00m00s [103/178] lua-srpm-macros-0:1-16.fc43.n 100% | 113.7 KiB/s | 8.8 KiB | 00m00s [104/178] ocaml-srpm-macros-0:11-2.fc43 100% | 132.3 KiB/s | 9.3 KiB | 00m00s [105/178] openblas-srpm-macros-0:2-20.f 100% | 135.6 KiB/s | 7.6 KiB | 00m00s [106/178] package-notes-srpm-macros-0:0 100% | 160.5 KiB/s | 9.0 KiB | 00m00s [107/178] perl-srpm-macros-0:1-60.fc43. 100% | 184.2 KiB/s | 8.3 KiB | 00m00s [108/178] python-srpm-macros-0:3.14-9.f 100% | 610.5 KiB/s | 23.8 KiB | 00m00s [109/178] tree-sitter-srpm-macros-0:0.4 100% | 290.2 KiB/s | 13.4 KiB | 00m00s [110/178] zig-srpm-macros-0:1-5.fc43.no 100% | 191.7 KiB/s | 8.4 KiB | 00m00s [111/178] zip-0:3.0-44.fc43.ppc64le 100% | 6.1 MiB/s | 275.0 KiB | 00m00s [112/178] pkgconf-0:2.3.0-3.fc43.ppc64l 100% | 1.1 MiB/s | 45.2 KiB | 00m00s [113/178] pkgconf-m4-0:2.3.0-3.fc43.noa 100% | 302.4 KiB/s | 13.9 KiB | 00m00s [114/178] libpkgconf-0:2.3.0-3.fc43.ppc 100% | 952.5 KiB/s | 42.9 KiB | 00m00s [115/178] ed-0:1.22.3-1.fc44.ppc64le 100% | 1.9 MiB/s | 86.8 KiB | 00m00s [116/178] libattr-0:2.5.2-6.fc43.ppc64l 100% | 381.2 KiB/s | 18.7 KiB | 00m00s [117/178] ncurses-base-0:6.5-7.20250614 100% | 1.6 MiB/s | 63.7 KiB | 00m00s [118/178] libsepol-0:3.9-2.fc43.ppc64le 100% | 7.4 MiB/s | 380.8 KiB | 00m00s [119/178] pcre2-0:10.47-1.fc44.ppc64le 100% | 6.6 MiB/s | 282.2 KiB | 00m00s [120/178] pcre2-syntax-0:10.47-1.fc44.n 100% | 3.3 MiB/s | 164.7 KiB | 00m00s [121/178] openssl-libs-1:3.5.4-1.fc44.p 100% | 32.2 MiB/s | 2.8 MiB | 00m00s [122/178] libxml2-0:2.12.10-5.fc44.ppc6 100% | 10.1 MiB/s | 776.5 KiB | 00m00s [123/178] lz4-libs-0:1.10.0-3.fc43.ppc6 100% | 1.8 MiB/s | 103.0 KiB | 00m00s [124/178] libassuan-0:2.5.7-4.fc43.ppc6 100% | 1.3 MiB/s | 71.5 KiB | 00m00s [125/178] gnupg2-verify-0:2.4.8-4.fc43. 100% | 3.2 MiB/s | 185.9 KiB | 00m00s [126/178] tpm2-tss-0:4.1.3-8.fc43.ppc64 100% | 6.2 MiB/s | 394.8 KiB | 00m00s [127/178] npth-0:1.8-3.fc43.ppc64le 100% | 267.9 KiB/s | 25.2 KiB | 00m00s [128/178] libgpg-error-0:1.56-1.fc44.pp 100% | 2.1 MiB/s | 253.7 KiB | 00m00s [129/178] libgcrypt-0:1.11.2-1.fc44.ppc 100% | 5.4 MiB/s | 665.1 KiB | 00m00s [130/178] gnupg2-dirmngr-0:2.4.8-4.fc43 100% | 5.4 MiB/s | 315.9 KiB | 00m00s [131/178] gnupg2-gpg-agent-0:2.4.8-4.fc 100% | 6.0 MiB/s | 306.0 KiB | 00m00s [132/178] gnupg2-gpgconf-0:2.4.8-4.fc43 100% | 2.5 MiB/s | 125.0 KiB | 00m00s [133/178] gnupg2-keyboxd-0:2.4.8-4.fc43 100% | 2.6 MiB/s | 103.4 KiB | 00m00s [134/178] gmp-1:6.3.0-4.fc44.ppc64le 100% | 6.5 MiB/s | 321.0 KiB | 00m00s [135/178] mpfr-0:4.2.2-2.fc43.ppc64le 100% | 6.9 MiB/s | 362.2 KiB | 00m00s [136/178] elfutils-default-yama-scope-0 100% | 238.0 KiB/s | 12.4 KiB | 00m00s [137/178] json-c-0:0.18-7.fc43.ppc64le 100% | 952.1 KiB/s | 49.5 KiB | 00m00s [138/178] gnulib-l10n-0:20241231-1.fc44 100% | 2.7 MiB/s | 143.0 KiB | 00m00s [139/178] alternatives-0:1.33-3.fc44.pp 100% | 909.3 KiB/s | 43.6 KiB | 00m00s [140/178] jansson-0:2.14-3.fc43.ppc64le 100% | 1.0 MiB/s | 50.7 KiB | 00m00s [141/178] libusb1-0:1.0.29-4.fc44.ppc64 100% | 1.7 MiB/s | 85.4 KiB | 00m00s [142/178] crypto-policies-0:20251128-1. 100% | 1.7 MiB/s | 74.7 KiB | 00m00s [143/178] libksba-0:1.6.7-4.fc43.ppc64l 100% | 3.3 MiB/s | 177.7 KiB | 00m00s [144/178] ca-certificates-0:2025.2.80_v 100% | 15.3 MiB/s | 973.8 KiB | 00m00s [145/178] gnutls-0:3.8.11-6.fc44.ppc64l 100% | 12.4 MiB/s | 1.4 MiB | 00m00s [146/178] openldap-0:2.6.10-4.fc44.ppc6 100% | 3.7 MiB/s | 289.5 KiB | 00m00s [147/178] libffi-0:3.5.2-1.fc44.ppc64le 100% | 538.2 KiB/s | 42.0 KiB | 00m00s [148/178] libtasn1-0:4.20.0-2.fc43.ppc6 100% | 1.2 MiB/s | 81.2 KiB | 00m00s [149/178] p11-kit-trust-0:0.25.8-1.fc44 100% | 2.0 MiB/s | 156.5 KiB | 00m00s [150/178] p11-kit-0:0.25.8-1.fc44.ppc64 100% | 6.3 MiB/s | 499.5 KiB | 00m00s [151/178] libtool-ltdl-0:2.5.4-8.fc44.p 100% | 522.1 KiB/s | 39.7 KiB | 00m00s [152/178] libevent-0:2.1.12-16.fc43.ppc 100% | 3.6 MiB/s | 274.4 KiB | 00m00s [153/178] cyrus-sasl-lib-0:2.1.28-33.fc 100% | 7.3 MiB/s | 901.7 KiB | 00m00s [154/178] libidn2-0:2.3.8-2.fc43.ppc64l 100% | 2.2 MiB/s | 171.3 KiB | 00m00s [155/178] libunistring-0:1.1-10.fc43.pp 100% | 7.2 MiB/s | 575.4 KiB | 00m00s [156/178] nettle-0:3.10.1-2.fc43.ppc64l 100% | 9.6 MiB/s | 461.0 KiB | 00m00s [157/178] gdbm-libs-1:1.23-10.fc43.ppc6 100% | 1.2 MiB/s | 61.8 KiB | 00m00s [158/178] fedora-release-0:44-0.8.noarc 100% | 289.8 KiB/s | 13.3 KiB | 00m00s [159/178] systemd-standalone-sysusers-0 100% | 2.2 MiB/s | 150.3 KiB | 00m00s [160/178] fedora-release-identity-basic 100% | 235.0 KiB/s | 14.1 KiB | 00m00s [161/178] xxhash-libs-0:0.8.3-3.fc43.pp 100% | 245.4 KiB/s | 37.8 KiB | 00m00s [162/178] libcurl-0:8.17.0-4.fc44.ppc64 100% | 7.2 MiB/s | 466.1 KiB | 00m00s [163/178] libnghttp2-0:1.68.0-2.fc44.pp 100% | 1.5 MiB/s | 80.3 KiB | 00m00s [164/178] gdb-minimal-0:16.3-6.fc44.ppc 100% | 17.9 MiB/s | 4.7 MiB | 00m00s [165/178] selinux-policy-0:42.19-1.fc44 100% | 1.2 MiB/s | 65.4 KiB | 00m00s [166/178] rpm-plugin-selinux-0:6.0.0-1. 100% | 500.2 KiB/s | 19.5 KiB | 00m00s [167/178] policycoreutils-0:3.9-5.fc44. 100% | 4.4 MiB/s | 193.8 KiB | 00m00s [168/178] libselinux-utils-0:3.9-5.fc44 100% | 1.6 MiB/s | 115.2 KiB | 00m00s [169/178] krb5-libs-0:1.21.3-10.fc44.pp 100% | 9.2 MiB/s | 844.6 KiB | 00m00s [170/178] libbrotli-0:1.1.0-10.fc44.ppc 100% | 8.3 MiB/s | 381.9 KiB | 00m00s [171/178] libpsl-0:0.21.5-6.fc43.ppc64l 100% | 1.5 MiB/s | 66.7 KiB | 00m00s [172/178] libssh-0:0.11.3-1.fc44.ppc64l 100% | 5.8 MiB/s | 266.5 KiB | 00m00s [173/178] libssh-config-0:0.11.3-1.fc44 100% | 222.2 KiB/s | 9.1 KiB | 00m00s [174/178] keyutils-libs-0:1.6.3-6.fc43. 100% | 692.4 KiB/s | 32.5 KiB | 00m00s [175/178] libcom_err-0:1.47.3-3.fc44.pp 100% | 494.7 KiB/s | 27.2 KiB | 00m00s [176/178] libverto-0:0.3.2-11.fc43.ppc6 100% | 312.7 KiB/s | 21.9 KiB | 00m00s [177/178] selinux-policy-targeted-0:42. 100% | 14.4 MiB/s | 6.6 MiB | 00m00s [178/178] publicsuffix-list-dafsa-0:202 100% | 778.3 KiB/s | 59.2 KiB | 00m00s -------------------------------------------------------------------------------- [178/178] Total 100% | 15.0 MiB/s | 71.2 MiB | 00m05s Running transaction [ 1/180] Verify package files 100% | 155.0 B/s | 178.0 B | 00m01s [ 2/180] Prepare transaction 100% | 1.0 KiB/s | 178.0 B | 00m00s [ 3/180] Installing libgcc-0:15.2.1-4. 100% | 70.3 MiB/s | 288.1 KiB | 00m00s [ 4/180] Installing publicsuffix-list- 100% | 34.1 MiB/s | 69.8 KiB | 00m00s [ 5/180] Installing libssh-config-0:0. 100% | 796.9 KiB/s | 816.0 B | 00m00s [ 6/180] Installing fedora-release-ide 100% | 898.4 KiB/s | 920.0 B | 00m00s [ 7/180] Installing fedora-gpg-keys-0: 100% | 11.7 MiB/s | 179.0 KiB | 00m00s [ 8/180] Installing fedora-repos-rawhi 100% | 2.4 MiB/s | 2.4 KiB | 00m00s [ 9/180] Installing fedora-repos-0:44- 100% | 5.6 MiB/s | 5.7 KiB | 00m00s [ 10/180] Installing fedora-release-com 100% | 8.2 MiB/s | 25.1 KiB | 00m00s [ 11/180] Installing fedora-release-0:4 100% | 1.6 KiB/s | 124.0 B | 00m00s >>> Running sysusers scriptlet: setup-0:2.15.0-27.fc44.noarch >>> Finished sysusers scriptlet: setup-0:2.15.0-27.fc44.noarch >>> Scriptlet output: >>> Creating group 'adm' with GID 4. >>> Creating group 'audio' with GID 63. >>> Creating group 'cdrom' with GID 11. >>> Creating group 'clock' with GID 103. >>> Creating group 'dialout' with GID 18. >>> Creating group 'disk' with GID 6. >>> Creating group 'floppy' with GID 19. >>> Creating group 'ftp' with GID 50. >>> Creating group 'games' with GID 20. >>> Creating group 'input' with GID 104. >>> Creating group 'kmem' with GID 9. >>> Creating group 'kvm' with GID 36. >>> Creating group 'lock' with GID 54. >>> Creating group 'lp' with GID 7. >>> Creating group 'mail' with GID 12. >>> Creating group 'man' with GID 15. >>> Creating group 'mem' with GID 8. >>> Creating group 'nobody' with GID 65534. >>> Creating group 'render' with GID 105. >>> Creating group 'root' with GID 0. >>> Creating group 'sgx' with GID 106. >>> Creating group 'sys' with GID 3. >>> Creating group 'tape' with GID 33. >>> Creating group 'tty' with GID 5. >>> Creating group 'users' with GID 100. >>> Creating group 'utmp' with GID 22. >>> Creating group 'video' with GID 39. >>> Creating group 'wheel' with GID 10. >>> Creating user 'adm' (adm) with UID 3 and GID 4. >>> Creating group 'bin' with GID 1. >>> Creating user 'bin' (bin) with UID 1 and GID 1. >>> Creating group 'daemon' with GID 2. >>> Creating user 'daemon' (daemon) with UID 2 and GID 2. >>> Creating user 'ftp' (FTP User) with UID 14 and GID 50. >>> Creating user 'games' (games) with UID 12 and GID 100. >>> Creating user 'halt' (halt) with UID 7 and GID 0. >>> Creating user 'lp' (lp) with UID 4 and GID 7. >>> Creating user 'mail' (mail) with UID 8 and GID 12. >>> Creating user 'nobody' (Kernel Overflow User) with UID 65534 and GID 65534. >>> Creating user 'operator' (operator) with UID 11 and GID 0. >>> Creating user 'root' (Super User) with UID 0 and GID 0. >>> Creating user 'shutdown' (shutdown) with UID 6 and GID 0. >>> Creating user 'sync' (sync) with UID 5 and GID 0. >>> [ 12/180] Installing setup-0:2.15.0-27. 100% | 7.8 MiB/s | 730.6 KiB | 00m00s >>> [RPM] /etc/hosts created as /etc/hosts.rpmnew [ 13/180] Installing filesystem-0:3.18- 100% | 648.6 KiB/s | 212.8 KiB | 00m00s [ 14/180] Installing gnulib-l10n-0:2024 100% | 64.6 MiB/s | 661.9 KiB | 00m00s [ 15/180] Installing coreutils-common-0 100% | 67.2 MiB/s | 11.2 MiB | 00m00s [ 16/180] Installing pcre2-syntax-0:10. 100% | 69.4 MiB/s | 284.3 KiB | 00m00s [ 17/180] Installing ncurses-base-0:6.5 100% | 5.6 MiB/s | 353.5 KiB | 00m00s [ 18/180] Installing bash-0:5.3.0-2.fc4 100% | 51.3 MiB/s | 8.9 MiB | 00m00s [ 19/180] Installing glibc-common-0:2.4 100% | 26.1 MiB/s | 1.5 MiB | 00m00s [ 20/180] Installing glibc-gconv-extra- 100% | 92.0 MiB/s | 18.6 MiB | 00m00s [ 21/180] Installing glibc-0:2.42.9000- 100% | 89.6 MiB/s | 11.7 MiB | 00m00s [ 22/180] Installing ncurses-libs-0:6.5 100% | 115.1 MiB/s | 1.5 MiB | 00m00s [ 23/180] Installing glibc-minimal-lang 100% | 0.0 B/s | 124.0 B | 00m00s [ 24/180] Installing zlib-ng-compat-0:2 100% | 96.8 MiB/s | 198.2 KiB | 00m00s [ 25/180] Installing bzip2-libs-0:1.0.8 100% | 67.2 MiB/s | 137.7 KiB | 00m00s [ 26/180] Installing libgpg-error-0:1.5 100% | 23.1 MiB/s | 1.0 MiB | 00m00s [ 27/180] Installing libstdc++-0:15.2.1 100% | 54.3 MiB/s | 3.9 MiB | 00m00s [ 28/180] Installing libassuan-0:2.5.7- 100% | 70.8 MiB/s | 217.6 KiB | 00m00s [ 29/180] Installing libgcrypt-0:1.11.2 100% | 116.0 MiB/s | 1.5 MiB | 00m00s [ 30/180] Installing readline-0:8.3-2.f 100% | 87.8 MiB/s | 629.7 KiB | 00m00s [ 31/180] Installing libuuid-0:2.41.2-9 100% | 68.6 MiB/s | 70.3 KiB | 00m00s [ 32/180] Installing xz-libs-1:5.8.1-4. 100% | 86.7 MiB/s | 266.4 KiB | 00m00s [ 33/180] Installing gmp-1:6.3.0-4.fc44 100% | 96.2 MiB/s | 788.4 KiB | 00m00s [ 34/180] Installing popt-0:1.19-9.fc43 100% | 17.5 MiB/s | 215.3 KiB | 00m00s [ 35/180] Installing libzstd-0:1.5.7-3. 100% | 121.3 MiB/s | 1.1 MiB | 00m00s [ 36/180] Installing elfutils-libelf-0: 100% | 134.1 MiB/s | 1.2 MiB | 00m00s [ 37/180] Installing npth-0:1.8-3.fc43. 100% | 92.0 MiB/s | 94.2 KiB | 00m00s [ 38/180] Installing libblkid-0:2.41.2- 100% | 115.7 MiB/s | 355.5 KiB | 00m00s [ 39/180] Installing libxcrypt-0:4.5.2- 100% | 82.9 MiB/s | 339.7 KiB | 00m00s [ 40/180] Installing systemd-libs-0:259 100% | 148.1 MiB/s | 3.0 MiB | 00m00s [ 41/180] Installing sqlite-libs-0:3.51 100% | 129.3 MiB/s | 1.9 MiB | 00m00s [ 42/180] Installing libsepol-0:3.9-2.f 100% | 130.2 MiB/s | 1.0 MiB | 00m00s [ 43/180] Installing gnupg2-gpgconf-0:2 100% | 9.3 MiB/s | 323.9 KiB | 00m00s [ 44/180] Installing libattr-0:2.5.2-6. 100% | 33.7 MiB/s | 69.1 KiB | 00m00s [ 45/180] Installing libacl-0:2.3.2-4.f 100% | 66.8 MiB/s | 68.4 KiB | 00m00s [ 46/180] Installing pcre2-0:10.47-1.fc 100% | 137.3 MiB/s | 843.4 KiB | 00m00s [ 47/180] Installing libselinux-0:3.9-5 100% | 86.7 MiB/s | 266.4 KiB | 00m00s [ 48/180] Installing grep-0:3.12-2.fc43 100% | 19.6 MiB/s | 1.0 MiB | 00m00s [ 49/180] Installing sed-0:4.9-6.fc44.p 100% | 14.9 MiB/s | 945.3 KiB | 00m00s [ 50/180] Installing findutils-1:4.10.0 100% | 35.9 MiB/s | 2.0 MiB | 00m00s [ 51/180] Installing libtasn1-0:4.20.0- 100% | 43.3 MiB/s | 221.9 KiB | 00m00s [ 52/180] Installing libunistring-0:1.1 100% | 52.0 MiB/s | 1.9 MiB | 00m00s [ 53/180] Installing libidn2-0:2.3.8-2. 100% | 6.6 MiB/s | 566.4 KiB | 00m00s [ 54/180] Installing crypto-policies-0: 100% | 9.6 MiB/s | 157.7 KiB | 00m00s [ 55/180] Installing xz-1:5.8.1-4.fc44. 100% | 27.7 MiB/s | 1.4 MiB | 00m00s [ 56/180] Installing libmount-0:2.41.2- 100% | 107.3 MiB/s | 549.3 KiB | 00m00s [ 57/180] Installing gnupg2-verify-0:2. 100% | 11.7 MiB/s | 429.7 KiB | 00m00s [ 58/180] Installing dwz-0:0.16-2.fc43. 100% | 7.3 MiB/s | 388.0 KiB | 00m00s [ 59/180] Installing mpfr-0:4.2.2-2.fc4 100% | 89.4 MiB/s | 915.3 KiB | 00m00s [ 60/180] Installing gawk-0:5.3.2-2.fc4 100% | 51.5 MiB/s | 2.8 MiB | 00m00s [ 61/180] Installing libksba-0:1.6.7-4. 100% | 64.6 MiB/s | 529.0 KiB | 00m00s [ 62/180] Installing unzip-0:6.0-68.fc4 100% | 11.7 MiB/s | 537.4 KiB | 00m00s [ 63/180] Installing file-libs-0:5.46-8 100% | 162.7 MiB/s | 11.9 MiB | 00m00s [ 64/180] Installing file-0:5.46-8.fc44 100% | 2.7 MiB/s | 141.6 KiB | 00m00s [ 65/180] Installing diffutils-0:3.12-3 100% | 17.4 MiB/s | 1.7 MiB | 00m00s [ 66/180] Installing libsmartcols-0:2.4 100% | 71.0 MiB/s | 290.6 KiB | 00m00s [ 67/180] Installing libcap-ng-0:0.8.5- 100% | 4.1 MiB/s | 162.1 KiB | 00m00s [ 68/180] Installing audit-libs-0:4.1.2 100% | 108.0 MiB/s | 553.2 KiB | 00m00s [ 69/180] Installing libsemanage-0:3.9- 100% | 103.9 MiB/s | 425.7 KiB | 00m00s [ 70/180] Installing libeconf-0:0.7.9-2 100% | 1.9 MiB/s | 82.5 KiB | 00m00s [ 71/180] Installing pam-libs-0:1.7.1-3 100% | 93.9 MiB/s | 288.6 KiB | 00m00s [ 72/180] Installing libcap-0:2.77-1.fc 100% | 10.7 MiB/s | 512.9 KiB | 00m00s [ 73/180] Installing lua-libs-0:5.4.8-4 100% | 35.1 MiB/s | 395.3 KiB | 00m00s [ 74/180] Installing json-c-0:0.18-7.fc 100% | 68.3 MiB/s | 139.8 KiB | 00m00s [ 75/180] Installing alternatives-0:1.3 100% | 2.0 MiB/s | 91.7 KiB | 00m00s [ 76/180] Installing libffi-0:3.5.2-1.f 100% | 113.6 MiB/s | 349.0 KiB | 00m00s [ 77/180] Installing p11-kit-0:0.25.8-1 100% | 18.1 MiB/s | 2.9 MiB | 00m00s [ 78/180] Installing p11-kit-trust-0:0. 100% | 3.7 MiB/s | 595.9 KiB | 00m00s [ 79/180] Installing openssl-libs-1:3.5 100% | 87.9 MiB/s | 9.0 MiB | 00m00s [ 80/180] Installing coreutils-0:9.9-1. 100% | 58.1 MiB/s | 9.4 MiB | 00m00s [ 81/180] Installing ca-certificates-0: 100% | 570.2 KiB/s | 2.5 MiB | 00m04s [ 82/180] Installing gzip-0:1.14-1.fc44 100% | 6.5 MiB/s | 443.1 KiB | 00m00s [ 83/180] Installing libfsverity-0:1.6- 100% | 33.7 MiB/s | 69.1 KiB | 00m00s [ 84/180] Installing rpm-sequoia-0:1.9. 100% | 83.3 MiB/s | 4.9 MiB | 00m00s [ 85/180] Installing libevent-0:2.1.12- 100% | 21.2 MiB/s | 1.2 MiB | 00m00s [ 86/180] Installing util-linux-core-0: 100% | 26.0 MiB/s | 2.5 MiB | 00m00s [ 87/180] Installing zip-0:3.0-44.fc43. 100% | 22.4 MiB/s | 893.7 KiB | 00m00s [ 88/180] Installing gnupg2-keyboxd-0:2 100% | 6.8 MiB/s | 298.5 KiB | 00m00s [ 89/180] Installing libpsl-0:0.21.5-6. 100% | 65.1 MiB/s | 133.2 KiB | 00m00s [ 90/180] Installing tar-2:1.35-6.fc43. 100% | 19.6 MiB/s | 3.1 MiB | 00m00s [ 91/180] Installing linkdupes-0:0.7.2- 100% | 17.1 MiB/s | 908.5 KiB | 00m00s [ 92/180] Installing libselinux-utils-0 100% | 32.3 MiB/s | 1.4 MiB | 00m00s [ 93/180] Installing liblastlog2-0:2.41 100% | 1.9 MiB/s | 139.6 KiB | 00m00s [ 94/180] Installing libusb1-0:1.0.29-4 100% | 4.0 MiB/s | 244.4 KiB | 00m00s >>> Running sysusers scriptlet: tpm2-tss-0:4.1.3-8.fc43.ppc64le >>> Finished sysusers scriptlet: tpm2-tss-0:4.1.3-8.fc43.ppc64le >>> Scriptlet output: >>> Creating group 'tss' with GID 59. >>> Creating user 'tss' (Account used for TPM access) with UID 59 and GID 59. >>> [ 95/180] Installing tpm2-tss-0:4.1.3-8 100% | 123.5 MiB/s | 2.5 MiB | 00m00s [ 96/180] Installing ima-evm-utils-libs 100% | 45.8 MiB/s | 93.9 KiB | 00m00s [ 97/180] Installing gnupg2-gpg-agent-0 100% | 6.9 MiB/s | 979.0 KiB | 00m00s [ 98/180] Installing systemd-standalone 100% | 6.3 MiB/s | 394.3 KiB | 00m00s [ 99/180] Installing rpm-libs-0:6.0.0-1 100% | 39.5 MiB/s | 1.2 MiB | 00m00s [100/180] Installing libfdisk-0:2.41.2- 100% | 118.2 MiB/s | 484.1 KiB | 00m00s [101/180] Installing zstd-0:1.5.7-3.fc4 100% | 15.7 MiB/s | 577.4 KiB | 00m00s [102/180] Installing nettle-0:3.10.1-2. 100% | 117.3 MiB/s | 960.9 KiB | 00m00s [103/180] Installing gnutls-0:3.8.11-6. 100% | 62.7 MiB/s | 4.1 MiB | 00m00s [104/180] Installing libxml2-0:2.12.10- 100% | 37.8 MiB/s | 2.3 MiB | 00m00s [105/180] Installing bzip2-0:1.0.8-21.f 100% | 4.9 MiB/s | 175.7 KiB | 00m00s [106/180] Installing add-determinism-0: 100% | 43.2 MiB/s | 2.4 MiB | 00m00s [107/180] Installing cpio-0:2.15-6.fc43 100% | 19.3 MiB/s | 1.2 MiB | 00m00s [108/180] Installing librtas-0:2.0.6-5. 100% | 5.3 MiB/s | 307.6 KiB | 00m00s [109/180] Installing util-linux-0:2.41. 100% | 19.0 MiB/s | 7.0 MiB | 00m00s [110/180] Installing policycoreutils-0: 100% | 11.2 MiB/s | 1.3 MiB | 00m00s [111/180] Installing selinux-policy-0:4 100% | 146.2 KiB/s | 33.6 KiB | 00m00s [112/180] Installing selinux-policy-tar 100% | 25.8 MiB/s | 14.9 MiB | 00m01s [113/180] Installing build-reproducibil 100% | 505.2 KiB/s | 1.5 KiB | 00m00s [114/180] Installing libgomp-0:15.2.1-4 100% | 12.9 MiB/s | 646.7 KiB | 00m00s [115/180] Installing libpkgconf-0:2.3.0 100% | 43.9 MiB/s | 135.0 KiB | 00m00s [116/180] Installing pkgconf-0:2.3.0-3. 100% | 1.5 MiB/s | 114.8 KiB | 00m00s [117/180] Installing ed-0:1.22.3-1.fc44 100% | 3.6 MiB/s | 159.1 KiB | 00m00s [118/180] Installing patch-0:2.8-2.fc43 100% | 6.3 MiB/s | 264.0 KiB | 00m00s [119/180] Installing lz4-libs-0:1.10.0- 100% | 64.0 MiB/s | 262.2 KiB | 00m00s [120/180] Installing libarchive-0:3.8.3 100% | 115.2 MiB/s | 1.3 MiB | 00m00s [121/180] Installing jansson-0:2.14-3.f 100% | 77.3 MiB/s | 158.4 KiB | 00m00s [122/180] Installing libtool-ltdl-0:2.5 100% | 46.4 MiB/s | 95.0 KiB | 00m00s [123/180] Installing gdbm-libs-1:1.23-1 100% | 76.6 MiB/s | 235.2 KiB | 00m00s [124/180] Installing cyrus-sasl-lib-0:2 100% | 53.0 MiB/s | 2.9 MiB | 00m00s [125/180] Installing openldap-0:2.6.10- 100% | 97.0 MiB/s | 893.7 KiB | 00m00s [126/180] Installing gnupg2-dirmngr-0:2 100% | 17.9 MiB/s | 840.8 KiB | 00m00s [127/180] Installing gnupg2-0:2.4.8-4.f 100% | 84.2 MiB/s | 6.9 MiB | 00m00s [128/180] Installing rpm-sign-libs-0:6. 100% | 22.3 MiB/s | 68.5 KiB | 00m00s [129/180] Installing gpgverify-0:2.2-3. 100% | 9.2 MiB/s | 9.4 KiB | 00m00s [130/180] Installing xxhash-libs-0:0.8. 100% | 42.5 MiB/s | 87.0 KiB | 00m00s [131/180] Installing libnghttp2-0:1.68. 100% | 64.8 MiB/s | 199.0 KiB | 00m00s [132/180] Installing libbrotli-0:1.1.0- 100% | 105.8 MiB/s | 975.0 KiB | 00m00s [133/180] Installing keyutils-libs-0:1. 100% | 48.5 MiB/s | 99.4 KiB | 00m00s [134/180] Installing libcom_err-0:1.47. 100% | 54.8 MiB/s | 112.1 KiB | 00m00s [135/180] Installing libverto-0:0.3.2-1 100% | 34.6 MiB/s | 70.9 KiB | 00m00s [136/180] Installing krb5-libs-0:1.21.3 100% | 123.7 MiB/s | 3.0 MiB | 00m00s [137/180] Installing libssh-0:0.11.3-1. 100% | 87.6 MiB/s | 717.3 KiB | 00m00s [138/180] Installing libcurl-0:8.17.0-4 100% | 114.5 MiB/s | 1.1 MiB | 00m00s [139/180] Installing curl-0:8.17.0-4.fc 100% | 10.5 MiB/s | 524.7 KiB | 00m00s [140/180] Installing rpm-0:6.0.0-1.fc44 100% | 32.8 MiB/s | 2.8 MiB | 00m00s [141/180] Installing efi-srpm-macros-0: 100% | 20.1 MiB/s | 41.2 KiB | 00m00s [142/180] Installing java-srpm-macros-0 100% | 1.1 MiB/s | 1.1 KiB | 00m00s [143/180] Installing lua-srpm-macros-0: 100% | 1.9 MiB/s | 1.9 KiB | 00m00s [144/180] Installing tree-sitter-srpm-m 100% | 9.1 MiB/s | 9.3 KiB | 00m00s [145/180] Installing zig-srpm-macros-0: 100% | 416.0 KiB/s | 1.7 KiB | 00m00s [146/180] Installing filesystem-srpm-ma 100% | 19.0 MiB/s | 38.9 KiB | 00m00s [147/180] Installing elfutils-default-y 100% | 75.7 KiB/s | 2.0 KiB | 00m00s [148/180] Installing elfutils-libs-0:0. 100% | 77.8 MiB/s | 876.5 KiB | 00m00s [149/180] Installing elfutils-debuginfo 100% | 3.1 MiB/s | 145.8 KiB | 00m00s [150/180] Installing elfutils-0:0.194-1 100% | 60.6 MiB/s | 3.5 MiB | 00m00s [151/180] Installing binutils-0:2.45.50 100% | 116.8 MiB/s | 32.6 MiB | 00m00s [152/180] Installing gdb-minimal-0:16.3 100% | 85.6 MiB/s | 15.7 MiB | 00m00s [153/180] Installing debugedit-0:5.2-3. 100% | 3.5 MiB/s | 384.9 KiB | 00m00s [154/180] Installing rpm-build-libs-0:6 100% | 80.3 MiB/s | 328.8 KiB | 00m00s [155/180] Installing pkgconf-m4-0:2.3.0 100% | 14.5 MiB/s | 14.8 KiB | 00m00s [156/180] Installing pkgconf-pkg-config 100% | 16.9 KiB/s | 1.8 KiB | 00m00s [157/180] Installing perl-srpm-macros-0 100% | 1.1 MiB/s | 1.1 KiB | 00m00s [158/180] Installing package-notes-srpm 100% | 2.0 MiB/s | 2.0 KiB | 00m00s [159/180] Installing openblas-srpm-macr 100% | 382.8 KiB/s | 392.0 B | 00m00s [160/180] Installing ocaml-srpm-macros- 100% | 2.1 MiB/s | 2.1 KiB | 00m00s [161/180] Installing kernel-srpm-macros 100% | 2.3 MiB/s | 2.3 KiB | 00m00s [162/180] Installing gnat-srpm-macros-0 100% | 1.2 MiB/s | 1.3 KiB | 00m00s [163/180] Installing ghc-srpm-macros-0: 100% | 1.0 MiB/s | 1.0 KiB | 00m00s [164/180] Installing gap-srpm-macros-0: 100% | 2.6 MiB/s | 2.7 KiB | 00m00s [165/180] Installing fpc-srpm-macros-0: 100% | 410.2 KiB/s | 420.0 B | 00m00s [166/180] Installing ansible-srpm-macro 100% | 35.4 MiB/s | 36.2 KiB | 00m00s [167/180] Installing rust-srpm-macros-0 100% | 6.2 MiB/s | 6.4 KiB | 00m00s [168/180] Installing qt6-srpm-macros-0: 100% | 722.7 KiB/s | 740.0 B | 00m00s [169/180] Installing qt5-srpm-macros-0: 100% | 757.8 KiB/s | 776.0 B | 00m00s [170/180] Installing redhat-rpm-config- 100% | 2.8 MiB/s | 189.9 KiB | 00m00s [171/180] Installing forge-srpm-macros- 100% | 19.7 MiB/s | 40.3 KiB | 00m00s [172/180] Installing fonts-srpm-macros- 100% | 27.8 MiB/s | 57.0 KiB | 00m00s [173/180] Installing go-srpm-macros-0:3 100% | 30.8 MiB/s | 63.0 KiB | 00m00s [174/180] Installing rpm-build-0:6.0.0- 100% | 5.4 MiB/s | 672.2 KiB | 00m00s [175/180] Installing pyproject-srpm-mac 100% | 2.4 MiB/s | 2.5 KiB | 00m00s [176/180] Installing python-srpm-macros 100% | 1.2 MiB/s | 52.9 KiB | 00m00s [177/180] Installing rpm-plugin-selinux 100% | 33.6 MiB/s | 68.9 KiB | 00m00s [178/180] Installing which-0:2.23-3.fc4 100% | 1.6 MiB/s | 125.5 KiB | 00m00s [179/180] Installing shadow-utils-2:4.1 100% | 18.5 MiB/s | 4.9 MiB | 00m00s [180/180] Installing info-0:7.2-7.fc44. 100% | 18.6 KiB/s | 485.8 KiB | 00m26s Warning: skipped OpenPGP checks for 178 packages from repository: http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch Complete! Finish: installing minimal buildroot with dnf5 Start: creating root cache Finish: creating root cache Finish: chroot init INFO: Installed packages: INFO: add-determinism-0.7.2-2.fc44.ppc64le alternatives-1.33-3.fc44.ppc64le ansible-srpm-macros-1-18.1.fc43.noarch audit-libs-4.1.2-2.fc44.ppc64le bash-5.3.0-2.fc43.ppc64le binutils-2.45.50-9.fc44.ppc64le build-reproducibility-srpm-macros-0.7.2-2.fc44.noarch bzip2-1.0.8-21.fc43.ppc64le bzip2-libs-1.0.8-21.fc43.ppc64le ca-certificates-2025.2.80_v9.0.304-2.fc44.noarch coreutils-9.9-1.fc44.ppc64le coreutils-common-9.9-1.fc44.ppc64le cpio-2.15-6.fc43.ppc64le crypto-policies-20251128-1.git19878fe.fc44.noarch curl-8.17.0-4.fc44.ppc64le cyrus-sasl-lib-2.1.28-33.fc44.ppc64le debugedit-5.2-3.fc44.ppc64le diffutils-3.12-3.fc43.ppc64le dwz-0.16-2.fc43.ppc64le ed-1.22.3-1.fc44.ppc64le efi-srpm-macros-6-5.fc44.noarch elfutils-0.194-1.fc44.ppc64le elfutils-debuginfod-client-0.194-1.fc44.ppc64le elfutils-default-yama-scope-0.194-1.fc44.noarch elfutils-libelf-0.194-1.fc44.ppc64le elfutils-libs-0.194-1.fc44.ppc64le fedora-gpg-keys-44-0.1.noarch fedora-release-44-0.8.noarch fedora-release-common-44-0.8.noarch fedora-release-identity-basic-44-0.8.noarch fedora-repos-44-0.1.noarch fedora-repos-rawhide-44-0.1.noarch file-5.46-8.fc44.ppc64le file-libs-5.46-8.fc44.ppc64le filesystem-3.18-50.fc43.ppc64le filesystem-srpm-macros-3.18-50.fc43.noarch findutils-4.10.0-6.fc43.ppc64le fonts-srpm-macros-5.0.0-1.fc44.noarch forge-srpm-macros-0.4.0-3.fc43.noarch fpc-srpm-macros-1.3-15.fc43.noarch gap-srpm-macros-2-1.fc44.noarch gawk-5.3.2-2.fc43.ppc64le gdb-minimal-16.3-6.fc44.ppc64le gdbm-libs-1.23-10.fc43.ppc64le ghc-srpm-macros-1.9.2-3.fc43.noarch glibc-2.42.9000-14.fc44.ppc64le glibc-common-2.42.9000-14.fc44.ppc64le glibc-gconv-extra-2.42.9000-14.fc44.ppc64le glibc-minimal-langpack-2.42.9000-14.fc44.ppc64le gmp-6.3.0-4.fc44.ppc64le gnat-srpm-macros-6-8.fc43.noarch gnulib-l10n-20241231-1.fc44.noarch gnupg2-2.4.8-4.fc43.ppc64le gnupg2-dirmngr-2.4.8-4.fc43.ppc64le gnupg2-gpg-agent-2.4.8-4.fc43.ppc64le gnupg2-gpgconf-2.4.8-4.fc43.ppc64le gnupg2-keyboxd-2.4.8-4.fc43.ppc64le gnupg2-verify-2.4.8-4.fc43.ppc64le gnutls-3.8.11-6.fc44.ppc64le go-srpm-macros-3.8.0-1.fc44.noarch gpgverify-2.2-3.fc43.noarch grep-3.12-2.fc43.ppc64le gzip-1.14-1.fc44.ppc64le ima-evm-utils-libs-1.6.2-7.fc44.ppc64le info-7.2-7.fc44.ppc64le jansson-2.14-3.fc43.ppc64le java-srpm-macros-1-7.fc43.noarch json-c-0.18-7.fc43.ppc64le kernel-srpm-macros-1.0-27.fc43.noarch keyutils-libs-1.6.3-6.fc43.ppc64le krb5-libs-1.21.3-10.fc44.ppc64le libacl-2.3.2-4.fc43.ppc64le libarchive-3.8.3-1.fc44.ppc64le libassuan-2.5.7-4.fc43.ppc64le libattr-2.5.2-6.fc43.ppc64le libblkid-2.41.2-9.fc44.ppc64le libbrotli-1.1.0-10.fc44.ppc64le libcap-2.77-1.fc44.ppc64le libcap-ng-0.8.5-8.fc44.ppc64le libcom_err-1.47.3-3.fc44.ppc64le libcurl-8.17.0-4.fc44.ppc64le libeconf-0.7.9-2.fc43.ppc64le libevent-2.1.12-16.fc43.ppc64le libfdisk-2.41.2-9.fc44.ppc64le libffi-3.5.2-1.fc44.ppc64le libfsverity-1.6-3.fc43.ppc64le libgcc-15.2.1-4.fc44.ppc64le libgcrypt-1.11.2-1.fc44.ppc64le libgomp-15.2.1-4.fc44.ppc64le libgpg-error-1.56-1.fc44.ppc64le libidn2-2.3.8-2.fc43.ppc64le libksba-1.6.7-4.fc43.ppc64le liblastlog2-2.41.2-9.fc44.ppc64le libmount-2.41.2-9.fc44.ppc64le libnghttp2-1.68.0-2.fc44.ppc64le libpkgconf-2.3.0-3.fc43.ppc64le libpsl-0.21.5-6.fc43.ppc64le librtas-2.0.6-5.fc44.ppc64le libselinux-3.9-5.fc44.ppc64le libselinux-utils-3.9-5.fc44.ppc64le libsemanage-3.9-4.fc44.ppc64le libsepol-3.9-2.fc43.ppc64le libsmartcols-2.41.2-9.fc44.ppc64le libssh-0.11.3-1.fc44.ppc64le libssh-config-0.11.3-1.fc44.noarch libstdc++-15.2.1-4.fc44.ppc64le libtasn1-4.20.0-2.fc43.ppc64le libtool-ltdl-2.5.4-8.fc44.ppc64le libunistring-1.1-10.fc43.ppc64le libusb1-1.0.29-4.fc44.ppc64le libuuid-2.41.2-9.fc44.ppc64le libverto-0.3.2-11.fc43.ppc64le libxcrypt-4.5.2-1.fc44.ppc64le libxml2-2.12.10-5.fc44.ppc64le libzstd-1.5.7-3.fc44.ppc64le linkdupes-0.7.2-2.fc44.ppc64le lua-libs-5.4.8-4.fc44.ppc64le lua-srpm-macros-1-16.fc43.noarch lz4-libs-1.10.0-3.fc43.ppc64le mpfr-4.2.2-2.fc43.ppc64le ncurses-base-6.5-7.20250614.fc43.noarch ncurses-libs-6.5-7.20250614.fc43.ppc64le nettle-3.10.1-2.fc43.ppc64le npth-1.8-3.fc43.ppc64le ocaml-srpm-macros-11-2.fc43.noarch openblas-srpm-macros-2-20.fc43.noarch openldap-2.6.10-4.fc44.ppc64le openssl-libs-3.5.4-1.fc44.ppc64le p11-kit-0.25.8-1.fc44.ppc64le p11-kit-trust-0.25.8-1.fc44.ppc64le package-notes-srpm-macros-0.5-14.fc43.noarch pam-libs-1.7.1-3.fc43.ppc64le patch-2.8-2.fc43.ppc64le pcre2-10.47-1.fc44.ppc64le pcre2-syntax-10.47-1.fc44.noarch perl-srpm-macros-1-60.fc43.noarch pkgconf-2.3.0-3.fc43.ppc64le pkgconf-m4-2.3.0-3.fc43.noarch pkgconf-pkg-config-2.3.0-3.fc43.ppc64le policycoreutils-3.9-5.fc44.ppc64le popt-1.19-9.fc43.ppc64le publicsuffix-list-dafsa-20250616-2.fc43.noarch pyproject-srpm-macros-1.18.6-1.fc44.noarch python-srpm-macros-3.14-9.fc44.noarch qt5-srpm-macros-5.15.18-1.fc44.noarch qt6-srpm-macros-6.10.1-1.fc44.noarch readline-8.3-2.fc43.ppc64le redhat-rpm-config-343-16.fc44.noarch rpm-6.0.0-1.fc44.ppc64le rpm-build-6.0.0-1.fc44.ppc64le rpm-build-libs-6.0.0-1.fc44.ppc64le rpm-libs-6.0.0-1.fc44.ppc64le rpm-plugin-selinux-6.0.0-1.fc44.ppc64le rpm-sequoia-1.9.0-2.fc43.ppc64le rpm-sign-libs-6.0.0-1.fc44.ppc64le rust-srpm-macros-28.2-1.fc44.noarch sed-4.9-6.fc44.ppc64le selinux-policy-42.19-1.fc44.noarch selinux-policy-targeted-42.19-1.fc44.noarch setup-2.15.0-27.fc44.noarch shadow-utils-4.18.0-7.fc44.ppc64le sqlite-libs-3.51.0-1.fc44.ppc64le systemd-libs-259~rc2-2.fc44.ppc64le systemd-standalone-sysusers-259~rc2-2.fc44.ppc64le tar-1.35-6.fc43.ppc64le tpm2-tss-4.1.3-8.fc43.ppc64le tree-sitter-srpm-macros-0.4.2-1.fc43.noarch unzip-6.0-68.fc44.ppc64le util-linux-2.41.2-9.fc44.ppc64le util-linux-core-2.41.2-9.fc44.ppc64le which-2.23-3.fc43.ppc64le xxhash-libs-0.8.3-3.fc43.ppc64le xz-5.8.1-4.fc44.ppc64le xz-libs-5.8.1-4.fc44.ppc64le zig-srpm-macros-1-5.fc43.noarch zip-3.0-44.fc43.ppc64le zlib-ng-compat-2.3.2-1.fc44.ppc64le zstd-1.5.7-3.fc44.ppc64le Start: buildsrpm Start: rpmbuild -bs Building target platforms: ppc64le Building for target ppc64le setting SOURCE_DATE_EPOCH=1764720000 Wrote: /builddir/build/SRPMS/python-scikit-learn-1.8.0~rc1-1.fc44.src.rpm Finish: rpmbuild -bs INFO: chroot_scan: 1 files copied to /var/lib/copr-rpmbuild/results/chroot_scan INFO: /var/lib/mock/fedora-43-ppc64le-1765205672.975907/root/var/log/dnf5.log INFO: chroot_scan: creating tarball /var/lib/copr-rpmbuild/results/chroot_scan.tar.gz /bin/tar: Removing leading `/' from member names Finish: buildsrpm INFO: Done(/var/lib/copr-rpmbuild/workspace/workdir-rh2752_3/python-scikit-learn/python-scikit-learn.spec) Config(child) 1 minutes 50 seconds INFO: Results and/or logs in: /var/lib/copr-rpmbuild/results INFO: Cleaning up build root ('cleanup_on_success=True') Start: clean chroot INFO: unmounting tmpfs. Finish: clean chroot INFO: Start(/var/lib/copr-rpmbuild/results/python-scikit-learn-1.8.0~rc1-1.fc44.src.rpm) Config(fedora-43-ppc64le) Start(bootstrap): chroot init INFO: mounting tmpfs at /var/lib/mock/fedora-43-ppc64le-bootstrap-1765205672.975907/root. INFO: reusing tmpfs at /var/lib/mock/fedora-43-ppc64le-bootstrap-1765205672.975907/root. INFO: calling preinit hooks INFO: enabled root cache INFO: enabled package manager cache Start(bootstrap): cleaning package manager metadata Finish(bootstrap): cleaning package manager metadata Finish(bootstrap): chroot init Start: chroot init INFO: mounting tmpfs at /var/lib/mock/fedora-43-ppc64le-1765205672.975907/root. INFO: calling preinit hooks INFO: enabled root cache Start: unpacking root cache Finish: unpacking root cache INFO: enabled package manager cache Start: cleaning package manager metadata Finish: cleaning package manager metadata INFO: enabled HW Info plugin INFO: Buildroot is handled by package management downloaded with a bootstrap image: rpm-6.0.0-1.fc43.ppc64le rpm-sequoia-1.9.0-2.fc43.ppc64le dnf5-5.2.17.0-2.fc43.ppc64le dnf5-plugins-5.2.17.0-2.fc43.ppc64le Finish: chroot init Start: build phase for python-scikit-learn-1.8.0~rc1-1.fc44.src.rpm Start: build setup for python-scikit-learn-1.8.0~rc1-1.fc44.src.rpm Building target platforms: ppc64le Building for target ppc64le setting SOURCE_DATE_EPOCH=1764720000 Wrote: /builddir/build/SRPMS/python-scikit-learn-1.8.0~rc1-1.fc44.src.rpm Updating and loading repositories: Additional repo http_kojipkgs_fedorapr 100% | 14.6 KiB/s | 3.8 KiB | 00m00s Copr repository 100% | 5.8 KiB/s | 1.5 KiB | 00m00s fedora 100% | 10.9 KiB/s | 6.1 KiB | 00m01s updates 100% | 16.5 KiB/s | 6.6 KiB | 00m00s Repositories loaded. Package Arch Version Repository Size Installing: gcc ppc64le 15.2.1-4.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 97.9 MiB gcc-c++ ppc64le 15.2.1-4.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 38.6 MiB python3-devel ppc64le 3.14.2-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.9 MiB python3-joblib noarch 1.5.2-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 2.3 MiB python3-psutil ppc64le 7.0.0-8.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.5 MiB python3-pytest noarch 8.4.2-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 22.5 MiB python3-setuptools noarch 80.9.0-2.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 8.6 MiB python3-threadpoolctl noarch 3.5.0-10.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 137.4 KiB Installing dependencies: annobin-docs noarch 13.03-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 99.2 KiB annobin-plugin-gcc ppc64le 13.03-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 707.6 KiB cpp ppc64le 15.2.1-4.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 34.6 MiB expat ppc64le 2.7.3-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 424.7 KiB gcc-plugin-annobin ppc64le 15.2.1-4.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 68.9 KiB glibc-devel ppc64le 2.42.9000-14.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 3.7 MiB kernel-headers ppc64le 6.18.0-65.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 6.7 MiB libasan ppc64le 15.2.1-4.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 2.1 MiB libatomic ppc64le 15.2.1-4.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 68.2 KiB libmpc ppc64le 1.3.1-8.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 281.6 KiB libstdc++-devel ppc64le 15.2.1-4.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 37.0 MiB libubsan ppc64le 15.2.1-4.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 668.6 KiB libxcrypt-devel ppc64le 4.5.2-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 31.0 KiB make ppc64le 1:4.4.1-11.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.9 MiB mpdecimal ppc64le 4.0.1-2.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 281.2 KiB pyproject-rpm-macros noarch 1.18.6-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 115.6 KiB python-pip-wheel noarch 25.3-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.2 MiB python-rpm-macros noarch 3.14-9.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 27.6 KiB python3 ppc64le 3.14.2-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 84.8 KiB python3-cloudpickle noarch 3.1.2-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 129.7 KiB python3-iniconfig noarch 2.3.0-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 49.3 KiB python3-libs ppc64le 3.14.2-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 46.4 MiB python3-packaging noarch 25.0-7.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 607.4 KiB python3-pluggy noarch 1.6.0-4.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 211.0 KiB python3-pygments noarch 2.19.1-7.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 11.3 MiB python3-rpm-generators noarch 14-13.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 81.7 KiB python3-rpm-macros noarch 3.14-9.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 6.5 KiB tzdata noarch 2025b-3.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.6 MiB Transaction Summary: Installing: 36 packages Total size of inbound packages is 89 MiB. Need to download 89 MiB. After this operation, 324 MiB extra will be used (install 324 MiB, remove 0 B). [ 1/36] python3-devel-0:3.14.2-1.fc44.p 100% | 617.3 KiB/s | 387.7 KiB | 00m01s [ 2/36] gcc-c++-0:15.2.1-4.fc44.ppc64le 100% | 14.1 MiB/s | 13.7 MiB | 00m01s [ 3/36] python3-psutil-0:7.0.0-8.fc44.p 100% | 5.6 MiB/s | 256.0 KiB | 00m00s [ 4/36] python3-joblib-0:1.5.2-1.fc44.n 100% | 1.2 MiB/s | 545.5 KiB | 00m00s [ 5/36] python3-pytest-0:8.4.2-1.fc44.n 100% | 24.0 MiB/s | 2.2 MiB | 00m00s [ 6/36] python3-setuptools-0:80.9.0-2.f 100% | 25.5 MiB/s | 1.8 MiB | 00m00s [ 7/36] python3-threadpoolctl-0:3.5.0-1 100% | 749.8 KiB/s | 45.0 KiB | 00m00s [ 8/36] cpp-0:15.2.1-4.fc44.ppc64le 100% | 34.1 MiB/s | 11.4 MiB | 00m00s [ 9/36] python3-0:3.14.2-1.fc44.ppc64le 100% | 328.3 KiB/s | 27.9 KiB | 00m00s [10/36] python3-libs-0:3.14.2-1.fc44.pp 100% | 23.7 MiB/s | 9.6 MiB | 00m00s [11/36] python3-iniconfig-0:2.3.0-1.fc4 100% | 491.3 KiB/s | 26.0 KiB | 00m00s [12/36] python3-packaging-0:25.0-7.fc44 100% | 3.1 MiB/s | 151.2 KiB | 00m00s [13/36] python3-pluggy-0:1.6.0-4.fc44.n 100% | 1.3 MiB/s | 56.1 KiB | 00m00s [14/36] python3-pygments-0:2.19.1-7.fc4 100% | 23.0 MiB/s | 2.5 MiB | 00m00s [15/36] expat-0:2.7.3-1.fc44.ppc64le 100% | 1.8 MiB/s | 125.7 KiB | 00m00s [16/36] mpdecimal-0:4.0.1-2.fc43.ppc64l 100% | 1.2 MiB/s | 112.4 KiB | 00m00s [17/36] python-pip-wheel-0:25.3-1.fc44. 100% | 9.3 MiB/s | 1.1 MiB | 00m00s [18/36] tzdata-0:2025b-3.fc43.noarch 100% | 3.8 MiB/s | 429.3 KiB | 00m00s [19/36] python3-cloudpickle-0:3.1.2-1.f 100% | 422.5 KiB/s | 48.2 KiB | 00m00s [20/36] libmpc-0:1.3.1-8.fc43.ppc64le 100% | 971.1 KiB/s | 96.1 KiB | 00m00s [21/36] glibc-devel-0:2.42.9000-14.fc44 100% | 4.9 MiB/s | 565.0 KiB | 00m00s [22/36] libasan-0:15.2.1-4.fc44.ppc64le 100% | 5.4 MiB/s | 546.2 KiB | 00m00s [23/36] libatomic-0:15.2.1-4.fc44.ppc64 100% | 547.4 KiB/s | 46.0 KiB | 00m00s [24/36] libubsan-0:15.2.1-4.fc44.ppc64l 100% | 3.6 MiB/s | 277.1 KiB | 00m00s [25/36] make-1:4.4.1-11.fc43.ppc64le 100% | 8.5 MiB/s | 595.2 KiB | 00m00s [26/36] kernel-headers-0:6.18.0-65.fc44 100% | 16.9 MiB/s | 1.5 MiB | 00m00s [27/36] libxcrypt-devel-0:4.5.2-1.fc44. 100% | 415.7 KiB/s | 29.9 KiB | 00m00s [28/36] gcc-plugin-annobin-0:15.2.1-4.f 100% | 863.4 KiB/s | 61.3 KiB | 00m00s [29/36] pyproject-rpm-macros-0:1.18.6-1 100% | 743.7 KiB/s | 44.6 KiB | 00m00s [30/36] python-rpm-macros-0:3.14-9.fc44 100% | 284.3 KiB/s | 19.6 KiB | 00m00s [31/36] python3-rpm-macros-0:3.14-9.fc4 100% | 264.5 KiB/s | 12.2 KiB | 00m00s [32/36] python3-rpm-generators-0:14-13. 100% | 396.3 KiB/s | 28.5 KiB | 00m00s [33/36] annobin-plugin-gcc-0:13.03-1.fc 100% | 13.1 MiB/s | 685.6 KiB | 00m00s [34/36] annobin-docs-0:13.03-1.fc44.noa 100% | 2.1 MiB/s | 89.4 KiB | 00m00s [35/36] libstdc++-devel-0:15.2.1-4.fc44 100% | 1.8 MiB/s | 5.1 MiB | 00m03s [36/36] gcc-0:15.2.1-4.fc44.ppc64le 100% | 4.1 MiB/s | 34.9 MiB | 00m09s -------------------------------------------------------------------------------- [36/36] Total 100% | 10.4 MiB/s | 88.9 MiB | 00m09s Running transaction [ 1/38] Verify package files 100% | 30.0 B/s | 36.0 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http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch Complete! Finish: build setup for python-scikit-learn-1.8.0~rc1-1.fc44.src.rpm Start: rpmbuild python-scikit-learn-1.8.0~rc1-1.fc44.src.rpm Building target platforms: ppc64le Building for target ppc64le setting SOURCE_DATE_EPOCH=1764720000 Executing(%mkbuilddir): /bin/sh -e /var/tmp/rpm-tmp.UtP89C Executing(%prep): /bin/sh -e /var/tmp/rpm-tmp.XcHK1a + umask 022 + cd /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build + cd /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build + rm -rf scikit_learn-1.8.0rc1 + /usr/lib/rpm/rpmuncompress -x /builddir/build/SOURCES/scikit_learn-1.8.0rc1.tar.gz + STATUS=0 + '[' 0 -ne 0 ']' + cd scikit_learn-1.8.0rc1 + /usr/bin/chmod -Rf a+rX,u+w,g-w,o-w . + sed -i -e 's|numpy>=2,<2.4.0|numpy|' pyproject.toml + sed -i -e 's|cython>=3.1.2|cython>=3.0.9|' pyproject.toml + sed -i -e 's|CYTHON_MIN_VERSION = "3.1.2"|CYTHON_MIN_VERSION = "3.0.9"|' sklearn/_min_dependencies.py + find sklearn/metrics/_dist_metrics.pyx.tp -type f + xargs sed -i 's/cdef inline {{INPUT_DTYPE_t}} rdist/cdef {{INPUT_DTYPE_t}} rdist/g' + RPM_EC=0 ++ jobs -p + exit 0 Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.5W16JE + umask 022 + cd /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build + cd scikit_learn-1.8.0rc1 + echo pyproject-rpm-macros + echo python3-devel + echo 'python3dist(packaging)' + echo 'python3dist(pip) >= 19' + '[' -f pyproject.toml ']' + echo '(python3dist(tomli) if python3-devel < 3.11)' + rm -rfv '*.dist-info/' + '[' -f /usr/bin/python3 ']' + mkdir -p /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/.pyproject-builddir + echo -n + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection ' + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection ' + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection -I/usr/lib64/gfortran/modules ' + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection -I/usr/lib64/gfortran/modules ' + VALAFLAGS=-g + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn' + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes ' + LT_SYS_LIBRARY_PATH=/usr/lib64: + CC=gcc + CXX=g++ + TMPDIR=/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/.pyproject-builddir + RPM_TOXENV=py314 + FEDORA=44 + HOSTNAME=rpmbuild + /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/pyproject-wheeldir --output /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/python-scikit-learn-1.8.0~rc1-1.fc44.ppc64le-pyproject-buildrequires -p Handling meson-python>=0.17.1,<0.19.0 from build-system.requires Requirement not satisfied: meson-python>=0.17.1,<0.19.0 Handling cython>=3.0.9,<3.3.0 from build-system.requires Requirement not satisfied: cython>=3.0.9,<3.3.0 Handling numpy from build-system.requires Requirement not satisfied: numpy Handling scipy>=1.10.0,<1.17.0 from build-system.requires Requirement not satisfied: scipy>=1.10.0,<1.17.0 Exiting dependency generation pass: build backend + cat /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/python-scikit-learn-1.8.0~rc1-1.fc44.ppc64le-pyproject-buildrequires + rm -rfv '*.dist-info/' + RPM_EC=0 ++ jobs -p + exit 0 Wrote: /builddir/build/SRPMS/python-scikit-learn-1.8.0~rc1-1.fc44.buildreqs.nosrc.rpm INFO: Going to install missing dynamic buildrequires Updating and loading repositories: Additional repo http_kojipkgs_fedorapr 100% | 3.1 KiB/s | 3.8 KiB | 00m01s Copr repository 100% | 1.2 KiB/s | 1.5 KiB | 00m01s fedora 100% | 4.0 KiB/s | 6.1 KiB | 00m02s updates 100% | 5.0 KiB/s | 6.6 KiB | 00m01s Repositories loaded. Package "gcc-15.2.1-4.fc44.ppc64le" is already installed.Package Arch Version Repository Size Installing: python3-cython ppc64le 3.2.1-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 20.4 MiB python3-meson-python noarch 0.18.0-7.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 326.5 KiB python3-numpy ppc64le 1:2.3.5-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 38.4 MiB python3-pip noarch 25.3-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 11.2 MiB python3-scipy ppc64le 1.16.2-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 74.6 MiB Installing dependencies: flexiblas ppc64le 3.5.0-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 38.0 KiB flexiblas-netlib ppc64le 3.5.0-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 19.2 MiB flexiblas-openblas-openmp ppc64le 3.5.0-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 66.9 KiB libgfortran ppc64le 15.2.1-4.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 2.8 MiB libquadmath ppc64le 15.2.1-4.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 426.5 KiB meson noarch 1.9.1-4.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 13.2 MiB ninja-build ppc64le 1.13.1-4.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 531.9 KiB openblas ppc64le 0.3.29-2.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 111.7 KiB openblas-openmp ppc64le 0.3.29-2.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 23.2 MiB patchelf ppc64le 0.18.0-9.fc43 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 370.9 KiB python3-charset-normalizer noarch 3.4.4-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 356.2 KiB python3-idna noarch 3.11-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 738.4 KiB python3-numpy-f2py ppc64le 1:2.3.5-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 2.1 MiB python3-platformdirs noarch 4.4.0-2.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 182.4 KiB python3-pooch noarch 1.8.2-9.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 635.2 KiB python3-pyproject-metadata noarch 0.10.0-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 207.7 KiB python3-requests noarch 2.32.5-2.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 476.9 KiB python3-urllib3 noarch 2.5.0-3.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 1.1 MiB vim-filesystem noarch 2:9.1.1952-1.fc44 http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch 40.0 B Transaction Summary: Installing: 24 packages Package "gcc-c++-15.2.1-4.fc44.ppc64le" is already installed. Package "pyproject-rpm-macros-1.18.6-1.fc44.noarch" is already installed. Package "python3-devel-3.14.2-1.fc44.ppc64le" is already installed. Package "python3-joblib-1.5.2-1.fc44.noarch" is already installed. Package "python3-packaging-25.0-7.fc44.noarch" is already installed. Package "python3-psutil-7.0.0-8.fc44.ppc64le" is already installed. Package "python3-pytest-8.4.2-1.fc44.noarch" is already installed. Package "python3-setuptools-80.9.0-2.fc44.noarch" is already installed. Package "python3-threadpoolctl-3.5.0-10.fc44.noarch" is already installed. Total size of inbound packages is 44 MiB. Need to download 44 MiB. After this operation, 211 MiB extra will be used (install 211 MiB, remove 0 B). [ 1/24] python3-meson-python-0:0.18.0-7 100% | 259.5 KiB/s | 88.5 KiB | 00m00s [ 2/24] python3-cython-0:3.2.1-1.fc44.p 100% | 5.1 MiB/s | 4.0 MiB | 00m01s [ 3/24] python3-pip-0:25.3-1.fc44.noarc 100% | 9.3 MiB/s | 2.4 MiB | 00m00s [ 4/24] python3-numpy-f2py-1:2.3.5-1.fc 100% | 6.0 MiB/s | 464.7 KiB | 00m00s [ 5/24] flexiblas-netlib-0:3.5.0-1.fc44 100% | 10.8 MiB/s | 4.4 MiB | 00m00s [ 6/24] python3-numpy-1:2.3.5-1.fc44.pp 100% | 5.7 MiB/s | 7.1 MiB | 00m01s [ 7/24] python3-scipy-0:1.16.2-1.fc44.p 100% | 9.7 MiB/s | 16.5 MiB | 00m02s [ 8/24] flexiblas-0:3.5.0-1.fc44.ppc64l 100% | 138.6 KiB/s | 23.0 KiB | 00m00s [ 9/24] flexiblas-openblas-openmp-0:3.5 100% | 125.8 KiB/s | 16.6 KiB | 00m00s [10/24] python3-pooch-0:1.8.2-9.fc44.no 100% | 1.8 MiB/s | 127.1 KiB | 00m00s [11/24] python3-platformdirs-0:4.4.0-2. 100% | 748.6 KiB/s | 44.2 KiB | 00m00s [12/24] libgfortran-0:15.2.1-4.fc44.ppc 100% | 5.9 MiB/s | 686.5 KiB | 00m00s [13/24] python3-requests-0:2.32.5-2.fc4 100% | 1.9 MiB/s | 150.2 KiB | 00m00s [14/24] patchelf-0:0.18.0-9.fc43.ppc64l 100% | 1.7 MiB/s | 133.0 KiB | 00m00s [15/24] python3-pyproject-metadata-0:0. 100% | 472.4 KiB/s | 55.7 KiB | 00m00s [16/24] ninja-build-0:1.13.1-4.fc44.ppc 100% | 2.0 MiB/s | 204.7 KiB | 00m00s [17/24] meson-0:1.9.1-4.fc44.noarch 100% | 9.1 MiB/s | 2.3 MiB | 00m00s [18/24] libquadmath-0:15.2.1-4.fc44.ppc 100% | 2.2 MiB/s | 224.7 KiB | 00m00s [19/24] python3-charset-normalizer-0:3. 100% | 1.3 MiB/s | 109.6 KiB | 00m00s [20/24] python3-idna-0:3.11-1.fc44.noar 100% | 1.7 MiB/s | 119.3 KiB | 00m00s [21/24] python3-urllib3-0:2.5.0-3.fc44. 100% | 2.8 MiB/s | 278.6 KiB | 00m00s [22/24] openblas-0:0.3.29-2.fc43.ppc64l 100% | 394.3 KiB/s | 42.2 KiB | 00m00s [23/24] vim-filesystem-2:9.1.1952-1.fc4 100% | 233.1 KiB/s | 15.4 KiB | 00m00s [24/24] openblas-openmp-0:0.3.29-2.fc43 100% | 11.9 MiB/s | 5.0 MiB | 00m00s -------------------------------------------------------------------------------- [24/24] Total 100% | 18.5 MiB/s | 44.5 MiB | 00m02s Running transaction [ 1/26] Verify package files 100% | 50.0 B/s | 24.0 B | 00m00s [ 2/26] Prepare transaction 100% | 130.0 B/s | 24.0 B | 00m00s [ 3/26] Installing libgfortran-0:15.2.1 100% | 118.3 MiB/s | 2.8 MiB | 00m00s [ 4/26] Installing python3-idna-0:3.11- 100% | 66.1 MiB/s | 744.9 KiB | 00m00s [ 5/26] Installing python3-urllib3-0:2. 100% | 74.1 MiB/s | 1.1 MiB | 00m00s [ 6/26] Installing vim-filesystem-2:9.1 100% | 2.3 MiB/s | 4.7 KiB | 00m00s [ 7/26] Installing ninja-build-0:1.13.1 100% | 11.9 MiB/s | 535.0 KiB | 00m00s [ 8/26] Installing meson-0:1.9.1-4.fc44 100% | 65.8 MiB/s | 13.4 MiB | 00m00s [ 9/26] Installing openblas-0:0.3.29-2. 100% | 36.9 MiB/s | 113.5 KiB | 00m00s [10/26] Installing openblas-openmp-0:0. 100% | 178.1 MiB/s | 23.2 MiB | 00m00s [11/26] Installing python3-charset-norm 100% | 9.2 MiB/s | 366.4 KiB | 00m00s [12/26] Installing python3-requests-0:2 100% | 53.0 MiB/s | 488.9 KiB | 00m00s [13/26] Installing libquadmath-0:15.2.1 100% | 104.4 MiB/s | 427.8 KiB | 00m00s [14/26] Installing flexiblas-netlib-0:3 100% | 111.6 MiB/s | 19.2 MiB | 00m00s [15/26] Installing flexiblas-0:3.5.0-1. 100% | 19.1 MiB/s | 39.2 KiB | 00m00s [16/26] Installing flexiblas-openblas-o 100% | 9.4 MiB/s | 67.7 KiB | 00m00s [17/26] Installing python3-numpy-1:2.3. 100% | 102.9 MiB/s | 38.7 MiB | 00m00s [18/26] Installing python3-numpy-f2py-1 100% | 31.0 MiB/s | 2.2 MiB | 00m00s [19/26] Installing python3-pyproject-me 100% | 52.0 MiB/s | 212.9 KiB | 00m00s [20/26] Installing patchelf-0:0.18.0-9. 100% | 10.1 MiB/s | 372.7 KiB | 00m00s [21/26] Installing python3-platformdirs 100% | 36.9 MiB/s | 188.8 KiB | 00m00s [22/26] Installing python3-pooch-0:1.8. 100% | 35.3 MiB/s | 650.4 KiB | 00m00s [23/26] Installing python3-scipy-0:1.16 100% | 95.9 MiB/s | 75.0 MiB | 00m01s [24/26] Installing python3-meson-python 100% | 29.5 MiB/s | 332.5 KiB | 00m00s [25/26] Installing python3-pip-0:25.3-1 100% | 52.4 MiB/s | 11.5 MiB | 00m00s [26/26] Installing python3-cython-0:3.2 100% | 18.8 MiB/s | 20.5 MiB | 00m01s Warning: skipped OpenPGP checks for 24 packages from repository: http_kojipkgs_fedoraproject_org_repos_rawhide_latest_basearch Complete! Building target platforms: ppc64le Building for target ppc64le setting SOURCE_DATE_EPOCH=1764720000 Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.ERkl1R + umask 022 + cd /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build + cd scikit_learn-1.8.0rc1 + echo pyproject-rpm-macros + echo python3-devel + echo 'python3dist(packaging)' + echo 'python3dist(pip) >= 19' + '[' -f pyproject.toml ']' + echo '(python3dist(tomli) if python3-devel < 3.11)' + rm -rfv '*.dist-info/' + '[' -f /usr/bin/python3 ']' + mkdir -p /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/.pyproject-builddir + echo -n + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection ' + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection ' + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection -I/usr/lib64/gfortran/modules ' + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection -I/usr/lib64/gfortran/modules ' + VALAFLAGS=-g + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn' + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes ' + LT_SYS_LIBRARY_PATH=/usr/lib64: + CC=gcc + CXX=g++ + TMPDIR=/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/.pyproject-builddir + RPM_TOXENV=py314 + FEDORA=44 + HOSTNAME=rpmbuild + /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/pyproject-wheeldir --output /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/python-scikit-learn-1.8.0~rc1-1.fc44.ppc64le-pyproject-buildrequires -p Handling meson-python>=0.17.1,<0.19.0 from build-system.requires Requirement satisfied: meson-python>=0.17.1,<0.19.0 (installed: meson-python 0.18.0) Handling cython>=3.0.9,<3.3.0 from build-system.requires Requirement satisfied: cython>=3.0.9,<3.3.0 (installed: cython 3.2.1) Handling numpy from build-system.requires Requirement satisfied: numpy (installed: numpy 2.3.5) Handling scipy>=1.10.0,<1.17.0 from build-system.requires Requirement satisfied: scipy>=1.10.0,<1.17.0 (installed: scipy 1.16.2) Handling numpy>=1.24.1 from pyproject.toml generated metadata: [dependencies] (scikit-learn) Requirement satisfied: numpy>=1.24.1 (installed: numpy 2.3.5) Handling scipy>=1.10.0 from pyproject.toml generated metadata: [dependencies] (scikit-learn) Requirement satisfied: scipy>=1.10.0 (installed: scipy 1.16.2) Handling joblib>=1.3.0 from pyproject.toml generated metadata: [dependencies] (scikit-learn) Requirement satisfied: joblib>=1.3.0 (installed: joblib 1.5.2) Handling threadpoolctl>=3.2.0 from pyproject.toml generated metadata: [dependencies] (scikit-learn) Requirement satisfied: threadpoolctl>=3.2.0 (installed: threadpoolctl 3.5.0) Handling numpy>=1.24.1 from pyproject.toml generated metadata: [optional-dependencies] build (scikit-learn) Ignoring alien requirement: numpy>=1.24.1 Handling scipy>=1.10.0 from pyproject.toml generated metadata: [optional-dependencies] build (scikit-learn) Ignoring alien requirement: scipy>=1.10.0 Handling cython>=3.0.9 from pyproject.toml generated metadata: [optional-dependencies] build (scikit-learn) Ignoring alien requirement: cython>=3.0.9 Handling meson-python>=0.17.1 from pyproject.toml generated metadata: [optional-dependencies] build (scikit-learn) Ignoring alien requirement: meson-python>=0.17.1 Handling numpy>=1.24.1 from pyproject.toml generated metadata: [optional-dependencies] install (scikit-learn) Ignoring alien requirement: numpy>=1.24.1 Handling scipy>=1.10.0 from pyproject.toml generated metadata: [optional-dependencies] install (scikit-learn) Ignoring alien requirement: scipy>=1.10.0 Handling joblib>=1.3.0 from pyproject.toml generated metadata: [optional-dependencies] install (scikit-learn) Ignoring alien requirement: joblib>=1.3.0 Handling threadpoolctl>=3.2.0 from pyproject.toml generated metadata: [optional-dependencies] install (scikit-learn) Ignoring alien requirement: threadpoolctl>=3.2.0 Handling matplotlib>=3.6.1 from pyproject.toml generated metadata: [optional-dependencies] benchmark (scikit-learn) Ignoring alien requirement: matplotlib>=3.6.1 Handling pandas>=1.5.0 from pyproject.toml generated metadata: [optional-dependencies] benchmark (scikit-learn) Ignoring alien requirement: pandas>=1.5.0 Handling memory_profiler>=0.57.0 from pyproject.toml generated metadata: [optional-dependencies] benchmark (scikit-learn) Ignoring alien requirement: memory_profiler>=0.57.0 Handling matplotlib>=3.6.1 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: matplotlib>=3.6.1 Handling scikit-image>=0.22.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: scikit-image>=0.22.0 Handling pandas>=1.5.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: pandas>=1.5.0 Handling seaborn>=0.13.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: seaborn>=0.13.0 Handling memory_profiler>=0.57.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: memory_profiler>=0.57.0 Handling sphinx>=7.3.7 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinx>=7.3.7 Handling sphinx-copybutton>=0.5.2 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinx-copybutton>=0.5.2 Handling sphinx-gallery>=0.17.1 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinx-gallery>=0.17.1 Handling numpydoc>=1.2.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: numpydoc>=1.2.0 Handling Pillow>=10.1.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: Pillow>=10.1.0 Handling pooch>=1.8.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: pooch>=1.8.0 Handling sphinx-prompt>=1.4.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinx-prompt>=1.4.0 Handling sphinxext-opengraph>=0.9.1 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinxext-opengraph>=0.9.1 Handling plotly>=5.18.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: plotly>=5.18.0 Handling polars>=0.20.30 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: polars>=0.20.30 Handling sphinx-design>=0.6.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinx-design>=0.6.0 Handling sphinxcontrib-sass>=0.3.4 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinxcontrib-sass>=0.3.4 Handling pydata-sphinx-theme>=0.15.3 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: pydata-sphinx-theme>=0.15.3 Handling sphinx-remove-toctrees>=1.0.0.post1 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinx-remove-toctrees>=1.0.0.post1 Handling towncrier>=24.8.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: towncrier>=24.8.0 Handling matplotlib>=3.6.1 from pyproject.toml generated metadata: [optional-dependencies] examples (scikit-learn) Ignoring alien requirement: matplotlib>=3.6.1 Handling scikit-image>=0.22.0 from pyproject.toml generated metadata: [optional-dependencies] examples (scikit-learn) Ignoring alien requirement: scikit-image>=0.22.0 Handling pandas>=1.5.0 from pyproject.toml generated metadata: [optional-dependencies] examples (scikit-learn) Ignoring alien requirement: pandas>=1.5.0 Handling seaborn>=0.13.0 from pyproject.toml generated metadata: [optional-dependencies] examples (scikit-learn) Ignoring alien requirement: seaborn>=0.13.0 Handling pooch>=1.8.0 from pyproject.toml generated metadata: [optional-dependencies] examples (scikit-learn) Ignoring alien requirement: pooch>=1.8.0 Handling plotly>=5.18.0 from pyproject.toml generated metadata: [optional-dependencies] examples (scikit-learn) Ignoring alien requirement: plotly>=5.18.0 Handling matplotlib>=3.6.1 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: matplotlib>=3.6.1 Handling pandas>=1.5.0 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: pandas>=1.5.0 Handling pytest>=7.1.2 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: pytest>=7.1.2 Handling pytest-cov>=2.9.0 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: pytest-cov>=2.9.0 Handling ruff>=0.11.7 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: ruff>=0.11.7 Handling mypy>=1.15 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: mypy>=1.15 Handling pyamg>=5.0.0 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: pyamg>=5.0.0 Handling polars>=0.20.30 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: polars>=0.20.30 Handling pyarrow>=12.0.0 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: pyarrow>=12.0.0 Handling numpydoc>=1.2.0 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: numpydoc>=1.2.0 Handling pooch>=1.8.0 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: pooch>=1.8.0 Handling conda-lock==3.0.1 from pyproject.toml generated metadata: [optional-dependencies] maintenance (scikit-learn) Ignoring alien requirement: conda-lock==3.0.1 + cat /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/python-scikit-learn-1.8.0~rc1-1.fc44.ppc64le-pyproject-buildrequires + rm -rfv '*.dist-info/' + RPM_EC=0 ++ jobs -p + exit 0 Wrote: /builddir/build/SRPMS/python-scikit-learn-1.8.0~rc1-1.fc44.buildreqs.nosrc.rpm INFO: Going to install missing dynamic buildrequires Updating and loading repositories: Additional repo http_kojipkgs_fedorapr 100% | 11.6 KiB/s | 3.8 KiB | 00m00s Copr repository 100% | 4.7 KiB/s | 1.5 KiB | 00m00s fedora 100% | 10.1 KiB/s | 6.1 KiB | 00m01s updates 100% | 14.3 KiB/s | 6.6 KiB | 00m00s Repositories loaded. Nothing to do. Package "gcc-15.2.1-4.fc44.ppc64le" is already installed. Package "gcc-c++-15.2.1-4.fc44.ppc64le" is already installed. Package "pyproject-rpm-macros-1.18.6-1.fc44.noarch" is already installed. Package "python3-devel-3.14.2-1.fc44.ppc64le" is already installed. Package "python3-joblib-1.5.2-1.fc44.noarch" is already installed. Package "python3-joblib-1.5.2-1.fc44.noarch" is already installed. Package "python3-numpy-1:2.3.5-1.fc44.ppc64le" is already installed. Package "python3-numpy-1:2.3.5-1.fc44.ppc64le" is already installed. Package "python3-packaging-25.0-7.fc44.noarch" is already installed. Package "python3-pip-25.3-1.fc44.noarch" is already installed. Package "python3-psutil-7.0.0-8.fc44.ppc64le" is already installed. Package "python3-pytest-8.4.2-1.fc44.noarch" is already installed. Package "python3-scipy-1.16.2-1.fc44.ppc64le" is already installed. Package "python3-setuptools-80.9.0-2.fc44.noarch" is already installed. Package "python3-threadpoolctl-3.5.0-10.fc44.noarch" is already installed. Package "python3-threadpoolctl-3.5.0-10.fc44.noarch" is already installed. Building target platforms: ppc64le Building for target ppc64le setting SOURCE_DATE_EPOCH=1764720000 Executing(%generate_buildrequires): /bin/sh -e /var/tmp/rpm-tmp.BOvu4S + umask 022 + cd /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build + cd scikit_learn-1.8.0rc1 + echo pyproject-rpm-macros + echo python3-devel + echo 'python3dist(packaging)' + echo 'python3dist(pip) >= 19' + '[' -f pyproject.toml ']' + echo '(python3dist(tomli) if python3-devel < 3.11)' + rm -rfv '*.dist-info/' + '[' -f /usr/bin/python3 ']' + mkdir -p /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/.pyproject-builddir + echo -n + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection ' + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection ' + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection -I/usr/lib64/gfortran/modules ' + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection -I/usr/lib64/gfortran/modules ' + VALAFLAGS=-g + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn' + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes ' + LT_SYS_LIBRARY_PATH=/usr/lib64: + CC=gcc + CXX=g++ + TMPDIR=/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/.pyproject-builddir + RPM_TOXENV=py314 + FEDORA=44 + HOSTNAME=rpmbuild + /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_buildrequires.py --generate-extras --python3_pkgversion 3 --wheeldir /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/pyproject-wheeldir --output /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/python-scikit-learn-1.8.0~rc1-1.fc44.ppc64le-pyproject-buildrequires -p Handling meson-python>=0.17.1,<0.19.0 from build-system.requires Requirement satisfied: meson-python>=0.17.1,<0.19.0 (installed: meson-python 0.18.0) Handling cython>=3.0.9,<3.3.0 from build-system.requires Requirement satisfied: cython>=3.0.9,<3.3.0 (installed: cython 3.2.1) Handling numpy from build-system.requires Requirement satisfied: numpy (installed: numpy 2.3.5) Handling scipy>=1.10.0,<1.17.0 from build-system.requires Requirement satisfied: scipy>=1.10.0,<1.17.0 (installed: scipy 1.16.2) Handling numpy>=1.24.1 from pyproject.toml generated metadata: [dependencies] (scikit-learn) Requirement satisfied: numpy>=1.24.1 (installed: numpy 2.3.5) Handling scipy>=1.10.0 from pyproject.toml generated metadata: [dependencies] (scikit-learn) Requirement satisfied: scipy>=1.10.0 (installed: scipy 1.16.2) Handling joblib>=1.3.0 from pyproject.toml generated metadata: [dependencies] (scikit-learn) Requirement satisfied: joblib>=1.3.0 (installed: joblib 1.5.2) Handling threadpoolctl>=3.2.0 from pyproject.toml generated metadata: [dependencies] (scikit-learn) Requirement satisfied: threadpoolctl>=3.2.0 (installed: threadpoolctl 3.5.0) Handling numpy>=1.24.1 from pyproject.toml generated metadata: [optional-dependencies] build (scikit-learn) Ignoring alien requirement: numpy>=1.24.1 Handling scipy>=1.10.0 from pyproject.toml generated metadata: [optional-dependencies] build (scikit-learn) Ignoring alien requirement: scipy>=1.10.0 Handling cython>=3.0.9 from pyproject.toml generated metadata: [optional-dependencies] build (scikit-learn) Ignoring alien requirement: cython>=3.0.9 Handling meson-python>=0.17.1 from pyproject.toml generated metadata: [optional-dependencies] build (scikit-learn) Ignoring alien requirement: meson-python>=0.17.1 Handling numpy>=1.24.1 from pyproject.toml generated metadata: [optional-dependencies] install (scikit-learn) Ignoring alien requirement: numpy>=1.24.1 Handling scipy>=1.10.0 from pyproject.toml generated metadata: [optional-dependencies] install (scikit-learn) Ignoring alien requirement: scipy>=1.10.0 Handling joblib>=1.3.0 from pyproject.toml generated metadata: [optional-dependencies] install (scikit-learn) Ignoring alien requirement: joblib>=1.3.0 Handling threadpoolctl>=3.2.0 from pyproject.toml generated metadata: [optional-dependencies] install (scikit-learn) Ignoring alien requirement: threadpoolctl>=3.2.0 Handling matplotlib>=3.6.1 from pyproject.toml generated metadata: [optional-dependencies] benchmark (scikit-learn) Ignoring alien requirement: matplotlib>=3.6.1 Handling pandas>=1.5.0 from pyproject.toml generated metadata: [optional-dependencies] benchmark (scikit-learn) Ignoring alien requirement: pandas>=1.5.0 Handling memory_profiler>=0.57.0 from pyproject.toml generated metadata: [optional-dependencies] benchmark (scikit-learn) Ignoring alien requirement: memory_profiler>=0.57.0 Handling matplotlib>=3.6.1 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: matplotlib>=3.6.1 Handling scikit-image>=0.22.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: scikit-image>=0.22.0 Handling pandas>=1.5.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: pandas>=1.5.0 Handling seaborn>=0.13.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: seaborn>=0.13.0 Handling memory_profiler>=0.57.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: memory_profiler>=0.57.0 Handling sphinx>=7.3.7 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinx>=7.3.7 Handling sphinx-copybutton>=0.5.2 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinx-copybutton>=0.5.2 Handling sphinx-gallery>=0.17.1 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinx-gallery>=0.17.1 Handling numpydoc>=1.2.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: numpydoc>=1.2.0 Handling Pillow>=10.1.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: Pillow>=10.1.0 Handling pooch>=1.8.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: pooch>=1.8.0 Handling sphinx-prompt>=1.4.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinx-prompt>=1.4.0 Handling sphinxext-opengraph>=0.9.1 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinxext-opengraph>=0.9.1 Handling plotly>=5.18.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: plotly>=5.18.0 Handling polars>=0.20.30 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: polars>=0.20.30 Handling sphinx-design>=0.6.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinx-design>=0.6.0 Handling sphinxcontrib-sass>=0.3.4 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinxcontrib-sass>=0.3.4 Handling pydata-sphinx-theme>=0.15.3 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: pydata-sphinx-theme>=0.15.3 Handling sphinx-remove-toctrees>=1.0.0.post1 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: sphinx-remove-toctrees>=1.0.0.post1 Handling towncrier>=24.8.0 from pyproject.toml generated metadata: [optional-dependencies] docs (scikit-learn) Ignoring alien requirement: towncrier>=24.8.0 Handling matplotlib>=3.6.1 from pyproject.toml generated metadata: [optional-dependencies] examples (scikit-learn) Ignoring alien requirement: matplotlib>=3.6.1 Handling scikit-image>=0.22.0 from pyproject.toml generated metadata: [optional-dependencies] examples (scikit-learn) Ignoring alien requirement: scikit-image>=0.22.0 Handling pandas>=1.5.0 from pyproject.toml generated metadata: [optional-dependencies] examples (scikit-learn) Ignoring alien requirement: pandas>=1.5.0 Handling seaborn>=0.13.0 from pyproject.toml generated metadata: [optional-dependencies] examples (scikit-learn) Ignoring alien requirement: seaborn>=0.13.0 Handling pooch>=1.8.0 from pyproject.toml generated metadata: [optional-dependencies] examples (scikit-learn) Ignoring alien requirement: pooch>=1.8.0 Handling plotly>=5.18.0 from pyproject.toml generated metadata: [optional-dependencies] examples (scikit-learn) Ignoring alien requirement: plotly>=5.18.0 Handling matplotlib>=3.6.1 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: matplotlib>=3.6.1 Handling pandas>=1.5.0 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: pandas>=1.5.0 Handling pytest>=7.1.2 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: pytest>=7.1.2 Handling pytest-cov>=2.9.0 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: pytest-cov>=2.9.0 Handling ruff>=0.11.7 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: ruff>=0.11.7 Handling mypy>=1.15 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: mypy>=1.15 Handling pyamg>=5.0.0 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: pyamg>=5.0.0 Handling polars>=0.20.30 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: polars>=0.20.30 Handling pyarrow>=12.0.0 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: pyarrow>=12.0.0 Handling numpydoc>=1.2.0 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: numpydoc>=1.2.0 Handling pooch>=1.8.0 from pyproject.toml generated metadata: [optional-dependencies] tests (scikit-learn) Ignoring alien requirement: pooch>=1.8.0 Handling conda-lock==3.0.1 from pyproject.toml generated metadata: [optional-dependencies] maintenance (scikit-learn) Ignoring alien requirement: conda-lock==3.0.1 + cat /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/python-scikit-learn-1.8.0~rc1-1.fc44.ppc64le-pyproject-buildrequires + rm -rfv '*.dist-info/' + RPM_EC=0 ++ jobs -p + exit 0 Executing(%build): /bin/sh -e /var/tmp/rpm-tmp.DDhmtv + umask 022 + cd /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection ' + export CFLAGS + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection ' + export CXXFLAGS + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection -I/usr/lib64/gfortran/modules ' + export FFLAGS + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection -I/usr/lib64/gfortran/modules ' + export FCFLAGS + VALAFLAGS=-g + export VALAFLAGS + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn' + export RUSTFLAGS + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes ' + export LDFLAGS + LT_SYS_LIBRARY_PATH=/usr/lib64: + export LT_SYS_LIBRARY_PATH + CC=gcc + export CC + CXX=g++ + export CXX + cd scikit_learn-1.8.0rc1 + mkdir -p /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/.pyproject-builddir + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection ' + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection ' + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection -I/usr/lib64/gfortran/modules ' + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection -I/usr/lib64/gfortran/modules ' + VALAFLAGS=-g + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn' + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes ' + LT_SYS_LIBRARY_PATH=/usr/lib64: + CC=gcc + CXX=g++ + TMPDIR=/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/.pyproject-builddir + /usr/bin/python3 -Bs /usr/lib/rpm/redhat/pyproject_wheel.py /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/pyproject-wheeldir Processing /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/scikit_learn-1.8.0rc1 Preparing metadata (pyproject.toml): started Running command Preparing metadata (pyproject.toml) + meson setup /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/scikit_learn-1.8.0rc1 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/scikit_learn-1.8.0rc1/.mesonpy-j9jjrz6b -Dbuildtype=release -Db_ndebug=if-release -Db_vscrt=md --native-file=/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/scikit_learn-1.8.0rc1/.mesonpy-j9jjrz6b/meson-python-native-file.ini The Meson build system Version: 1.9.1 Source dir: /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/scikit_learn-1.8.0rc1 Build dir: /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/scikit_learn-1.8.0rc1/.mesonpy-j9jjrz6b Build type: native build Project name: scikit-learn Project version: 1.8.0rc1 C compiler for the host machine: gcc (gcc 15.2.1 "gcc (GCC) 15.2.1 20251111 (Red Hat 15.2.1-4)") C linker for the host machine: gcc ld.bfd 2.45.50.20251124 C++ compiler for the host machine: g++ (gcc 15.2.1 "g++ (GCC) 15.2.1 20251111 (Red Hat 15.2.1-4)") C++ linker for the host machine: g++ ld.bfd 2.45.50.20251124 Cython compiler for the host machine: cython (cython 3.2.1) Host machine cpu family: ppc64 Host machine cpu: ppc64le Compiler for C supports arguments -Wno-unused-but-set-variable: YES Compiler for C supports arguments -Wno-unused-function: YES Compiler for C supports arguments -Wno-conversion: YES Compiler for C supports arguments -Wno-misleading-indentation: YES Library m found: YES Program sklearn/_build_utils/tempita.py found: YES (/usr/bin/python3 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/scikit_learn-1.8.0rc1/sklearn/_build_utils/tempita.py) Program python found: YES (/usr/bin/python3) Run-time dependency OpenMP for c found: YES 4.5 Program cython found: YES (/usr/bin/cython) Found pkg-config: YES (/usr/bin/pkg-config) 2.3.0 Run-time dependency python found: YES 3.14 Build targets in project: 112 scikit-learn 1.8.0rc1 User defined options Native files: /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/scikit_learn-1.8.0rc1/.mesonpy-j9jjrz6b/meson-python-native-file.ini b_ndebug : if-release b_vscrt : md buildtype : release Found ninja-1.13.1 at /usr/bin/ninja + /usr/bin/ninja [1/253] Copying file sklearn/_loss/_loss.pxd [2/253] Copying file sklearn/__init__.py [3/253] Copying file sklearn/utils/__init__.py [4/253] Copying file sklearn/utils/_cython_blas.pxd [5/253] Copying file sklearn/utils/_random.pxd [6/253] Copying file sklearn/utils/_openmp_helpers.pxd [7/253] Copying file sklearn/utils/_heap.pxd [8/253] Copying file sklearn/utils/_sorting.pxd [9/253] Copying file sklearn/utils/_typedefs.pxd [10/253] Copying file sklearn/utils/_vector_sentinel.pxd [11/253] Generating sklearn/utils/_seq_dataset_pxd with a custom command [12/253] Generating sklearn/utils/_weight_vector_pxd with a custom command [13/253] Copying file sklearn/metrics/__init__.py [14/253] Generating sklearn/metrics/_dist_metrics_pxd with a custom command [15/253] Copying file sklearn/metrics/_pairwise_distances_reduction/__init__.py [16/253] Copying file sklearn/metrics/_pairwise_distances_reduction/_classmode.pxd [17/253] Generating sklearn/metrics/_pairwise_distances_reduction/_base_pxd with a custom command [18/253] Generating sklearn/metrics/_pairwise_distances_reduction/_datasets_pair_pxd with a custom command [19/253] Generating sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer_pxd with a custom command [20/253] Generating sklearn/metrics/_pairwise_distances_reduction/_argkmin_pxd with a custom command [21/253] Generating sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_pxd with a custom command [22/253] Copying file sklearn/linear_model/__init__.py [23/253] Generating sklearn/neighbors/_binary_tree_pxi with a custom command [24/253] Copying file sklearn/neighbors/__init__.py [25/253] Copying file sklearn/neighbors/_partition_nodes.pxd [26/253] Generating sklearn/_loss/_loss_pyx with a custom command [27/253] Generating sklearn/utils/_seq_dataset_pyx with a custom command [28/253] Generating sklearn/utils/_weight_vector_pyx with a custom command [29/253] Generating sklearn/metrics/_pairwise_distances_reduction/_datasets_pair_pyx with a custom command [30/253] Generating sklearn/metrics/_dist_metrics_pyx with a custom command [31/253] Generating sklearn/metrics/_pairwise_distances_reduction/_argkmin_pyx with a custom command [32/253] Generating sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_pyx with a custom command [33/253] Generating sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode_pyx with a custom command [34/253] Generating sklearn/metrics/_pairwise_distances_reduction/_base_pyx with a custom command [35/253] Generating sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer_pyx with a custom command [36/253] Generating sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode_pyx with a custom command [37/253] Generating sklearn/neighbors/_ball_tree_pyx with a custom command [38/253] Generating sklearn/neighbors/_kd_tree_pyx with a custom command [39/253] Generating sklearn/linear_model/_sgd_fast_pyx with a custom command [40/253] Generating sklearn/linear_model/_sag_fast_pyx with a custom command [41/253] Generating 'sklearn/__check_build/_check_build.cpython-314-powerpc64le-linux-gnu.so.p/_check_build.c' [42/253] Generating sklearn/_cyutility.c with a custom command [43/253] Generating 'sklearn/_isotonic.cpython-314-powerpc64le-linux-gnu.so.p/_isotonic.c' [44/253] Generating 'sklearn/utils/_cython_blas.cpython-314-powerpc64le-linux-gnu.so.p/_cython_blas.c' [45/253] Generating 'sklearn/utils/sparsefuncs_fast.cpython-314-powerpc64le-linux-gnu.so.p/sparsefuncs_fast.c' [46/253] Generating 'sklearn/utils/arrayfuncs.cpython-314-powerpc64le-linux-gnu.so.p/arrayfuncs.c' [47/253] Generating 'sklearn/utils/murmurhash.cpython-314-powerpc64le-linux-gnu.so.p/murmurhash.c' [48/253] Generating 'sklearn/utils/_openmp_helpers.cpython-314-powerpc64le-linux-gnu.so.p/_openmp_helpers.c' [49/253] Generating 'sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c' [50/253] Generating 'sklearn/utils/_fast_dict.cpython-314-powerpc64le-linux-gnu.so.p/_fast_dict.cpp' [51/253] Generating 'sklearn/utils/_typedefs.cpython-314-powerpc64le-linux-gnu.so.p/_typedefs.c' [52/253] Generating 'sklearn/utils/_heap.cpython-314-powerpc64le-linux-gnu.so.p/_heap.c' [53/253] Generating 'sklearn/utils/_random.cpython-314-powerpc64le-linux-gnu.so.p/_random.c' [54/253] Generating 'sklearn/utils/_sorting.cpython-314-powerpc64le-linux-gnu.so.p/_sorting.c' [55/253] Generating 'sklearn/utils/_isfinite.cpython-314-powerpc64le-linux-gnu.so.p/_isfinite.c' [56/253] Generating 'sklearn/utils/_vector_sentinel.cpython-314-powerpc64le-linux-gnu.so.p/_vector_sentinel.cpp' [57/253] Generating 'sklearn/utils/_seq_dataset.cpython-314-powerpc64le-linux-gnu.so.p/_seq_dataset.c' [58/253] Generating 'sklearn/utils/_weight_vector.cpython-314-powerpc64le-linux-gnu.so.p/_weight_vector.c' [59/253] Generating 'sklearn/metrics/_pairwise_fast.cpython-314-powerpc64le-linux-gnu.so.p/_pairwise_fast.c' [60/253] Generating 'sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.cpython-314-powerpc64le-linux-gnu.so.p/_datasets_pair.cpp' [61/253] Generating 'sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp' [62/253] Generating 'sklearn/metrics/_dist_metrics.cpython-314-powerpc64le-linux-gnu.so.p/_dist_metrics.c' [63/253] Generating 'sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.cpython-314-powerpc64le-linux-gnu.so.p/_middle_term_computer.cpp' [64/253] Generating 'sklearn/metrics/_pairwise_distances_reduction/_argkmin.cpython-314-powerpc64le-linux-gnu.so.p/_argkmin.cpp' [65/253] Generating 'sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.cpython-314-powerpc64le-linux-gnu.so.p/_radius_neighbors.cpp' [66/253] Generating 'sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode.cpython-314-powerpc64le-linux-gnu.so.p/_argkmin_classmode.cpp' [67/253] Generating 'sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.cpython-314-powerpc64le-linux-gnu.so.p/_radius_neighbors_classmode.cpp' [68/253] Generating 'sklearn/metrics/cluster/_expected_mutual_info_fast.cpython-314-powerpc64le-linux-gnu.so.p/_expected_mutual_info_fast.c' [69/253] Generating 'sklearn/cluster/_dbscan_inner.cpython-314-powerpc64le-linux-gnu.so.p/_dbscan_inner.cpp' [70/253] Generating 'sklearn/cluster/_hierarchical_fast.cpython-314-powerpc64le-linux-gnu.so.p/_hierarchical_fast.cpp' [71/253] Generating 'sklearn/cluster/_k_means_common.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_common.c' [72/253] Generating 'sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c' [73/253] Generating 'sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c' [74/253] Generating 'sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c' [75/253] Generating 'sklearn/cluster/_hdbscan/_linkage.cpython-314-powerpc64le-linux-gnu.so.p/_linkage.c' [76/253] Generating 'sklearn/cluster/_hdbscan/_reachability.cpython-314-powerpc64le-linux-gnu.so.p/_reachability.c' [77/253] Generating 'sklearn/cluster/_hdbscan/_tree.cpython-314-powerpc64le-linux-gnu.so.p/_tree.c' [78/253] Generating 'sklearn/decomposition/_online_lda_fast.cpython-314-powerpc64le-linux-gnu.so.p/_online_lda_fast.c' [79/253] Generating 'sklearn/decomposition/_cdnmf_fast.cpython-314-powerpc64le-linux-gnu.so.p/_cdnmf_fast.c' [80/253] Generating 'sklearn/datasets/_svmlight_format_fast.cpython-314-powerpc64le-linux-gnu.so.p/_svmlight_format_fast.c' [81/253] Generating 'sklearn/ensemble/_hist_gradient_boosting/_gradient_boosting.cpython-314-powerpc64le-linux-gnu.so.p/_gradient_boosting.c' [82/253] Generating 'sklearn/ensemble/_gradient_boosting.cpython-314-powerpc64le-linux-gnu.so.p/_gradient_boosting.c' [83/253] Generating 'sklearn/ensemble/_hist_gradient_boosting/histogram.cpython-314-powerpc64le-linux-gnu.so.p/histogram.c' [84/253] Generating 'sklearn/ensemble/_hist_gradient_boosting/splitting.cpython-314-powerpc64le-linux-gnu.so.p/splitting.c' [85/253] Generating 'sklearn/ensemble/_hist_gradient_boosting/_binning.cpython-314-powerpc64le-linux-gnu.so.p/_binning.c' [86/253] Generating 'sklearn/ensemble/_hist_gradient_boosting/_predictor.cpython-314-powerpc64le-linux-gnu.so.p/_predictor.c' [87/253] Generating 'sklearn/ensemble/_hist_gradient_boosting/_bitset.cpython-314-powerpc64le-linux-gnu.so.p/_bitset.c' [88/253] Generating 'sklearn/ensemble/_hist_gradient_boosting/common.cpython-314-powerpc64le-linux-gnu.so.p/common.c' [89/253] Generating 'sklearn/feature_extraction/_hashing_fast.cpython-314-powerpc64le-linux-gnu.so.p/_hashing_fast.cpp' [90/253] Generating 'sklearn/linear_model/_sgd_fast.cpython-314-powerpc64le-linux-gnu.so.p/_sgd_fast.c' [91/253] Generating 'sklearn/linear_model/_cd_fast.cpython-314-powerpc64le-linux-gnu.so.p/_cd_fast.c' [92/253] Generating 'sklearn/linear_model/_sag_fast.cpython-314-powerpc64le-linux-gnu.so.p/_sag_fast.c' [93/253] Generating 'sklearn/manifold/_utils.cpython-314-powerpc64le-linux-gnu.so.p/_utils.c' [94/253] Generating 'sklearn/manifold/_barnes_hut_tsne.cpython-314-powerpc64le-linux-gnu.so.p/_barnes_hut_tsne.c' [95/253] Generating 'sklearn/neighbors/_partition_nodes.cpython-314-powerpc64le-linux-gnu.so.p/_partition_nodes.cpp' [96/253] Generating 'sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c' warning: sklearn/neighbors/_binary_tree.pxi:1091:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:1957:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:2052:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:2221:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:2344:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:2401:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:2402:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:2726:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:3592:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:3687:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:3856:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:3979:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:4036:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:4037:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_ball_tree.pyx:107:4: Assigning to 'float64_t *' from 'const float64_t *' discards const qualifier warning: sklearn/neighbors/_ball_tree.pyx:310:4: Assigning to 'float32_t *' from 'const float32_t *' discards const qualifier [97/253] Generating 'sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c' warning: sklearn/neighbors/_binary_tree.pxi:1091:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:1957:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:2052:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:2221:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:2344:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:2401:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:2402:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:2726:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:3592:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:3687:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:3856:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:3979:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:4036:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_binary_tree.pxi:4037:8: Assigning to 'intp_t *' from 'const intp_t *' discards const qualifier warning: sklearn/neighbors/_kd_tree.pyx:74:4: Assigning to 'float64_t *' from 'const float64_t *' discards const qualifier warning: sklearn/neighbors/_kd_tree.pyx:75:4: Assigning to 'float64_t *' from 'const float64_t *' discards const qualifier warning: sklearn/neighbors/_kd_tree.pyx:342:4: Assigning to 'float32_t *' from 'const float32_t *' discards const qualifier warning: sklearn/neighbors/_kd_tree.pyx:343:4: Assigning to 'float32_t *' from 'const float32_t *' discards const qualifier [98/253] Generating 'sklearn/neighbors/_quad_tree.cpython-314-powerpc64le-linux-gnu.so.p/_quad_tree.c' [99/253] Generating 'sklearn/preprocessing/_csr_polynomial_expansion.cpython-314-powerpc64le-linux-gnu.so.p/_csr_polynomial_expansion.c' [100/253] Generating 'sklearn/preprocessing/_target_encoder_fast.cpython-314-powerpc64le-linux-gnu.so.p/_target_encoder_fast.cpp' [101/253] Generating 'sklearn/svm/_newrand.cpython-314-powerpc64le-linux-gnu.so.p/_newrand.cpp' [102/253] Generating 'sklearn/svm/_libsvm.cpython-314-powerpc64le-linux-gnu.so.p/_libsvm.c' [103/253] Generating 'sklearn/svm/_libsvm_sparse.cpython-314-powerpc64le-linux-gnu.so.p/_libsvm_sparse.c' [104/253] Compiling C++ object sklearn/svm/libliblinear-skl.a.p/src_liblinear_tron.cpp.o [105/253] Generating 'sklearn/svm/_liblinear.cpython-314-powerpc64le-linux-gnu.so.p/_liblinear.c' [106/253] Compiling C++ object sklearn/svm/libliblinear-skl.a.p/src_liblinear_linear.cpp.o [107/253] Compiling C++ object sklearn/svm/liblibsvm-skl.a.p/src_libsvm_libsvm_template.cpp.o In file included from ../sklearn/svm/src/libsvm/libsvm_template.cpp:6: ../sklearn/svm/src/libsvm/svm.cpp: In function ‘svm::svm_train_one(svm_problem const*, svm_parameter const*, double, double, int*, BlasFunctions*)’: ../sklearn/svm/src/libsvm/svm.cpp:1883:13: warning: ‘si.obj’ may be used uninitialized [-Wmaybe-uninitialized] 1883 | info("obj = %f, rho = %f\n",si.obj,si.rho); | ~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ../sklearn/svm/src/libsvm/svm.cpp:1856:30: note: ‘si’ declared here 1856 | Solver::SolutionInfo si; | ^~ ../sklearn/svm/src/libsvm/svm.cpp:1883:13: warning: ‘si.rho’ may be used uninitialized [-Wmaybe-uninitialized] 1883 | info("obj = %f, rho = %f\n",si.obj,si.rho); | ~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ../sklearn/svm/src/libsvm/svm.cpp:1856:30: note: ‘si’ declared here 1856 | Solver::SolutionInfo si; | ^~ In file included from ../sklearn/svm/src/libsvm/libsvm_template.cpp:8: ../sklearn/svm/src/libsvm/svm.cpp: In function ‘svm_csr::svm_train_one(svm_csr_problem const*, svm_parameter const*, double, double, int*, BlasFunctions*)’: ../sklearn/svm/src/libsvm/svm.cpp:1883:13: warning: ‘si.obj’ may be used uninitialized [-Wmaybe-uninitialized] 1883 | info("obj = %f, rho = %f\n",si.obj,si.rho); | ~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ../sklearn/svm/src/libsvm/svm.cpp:1856:30: note: ‘si’ declared here 1856 | Solver::SolutionInfo si; | ^~ ../sklearn/svm/src/libsvm/svm.cpp:1883:13: warning: ‘si.rho’ may be used uninitialized [-Wmaybe-uninitialized] 1883 | info("obj = %f, rho = %f\n",si.obj,si.rho); | ~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ../sklearn/svm/src/libsvm/svm.cpp:1856:30: note: ‘si’ declared here 1856 | Solver::SolutionInfo si; | ^~ [108/253] Generating 'sklearn/tree/_splitter.cpython-314-powerpc64le-linux-gnu.so.p/_splitter.c' [109/253] Generating 'sklearn/tree/_tree.cpython-314-powerpc64le-linux-gnu.so.p/_tree.cpp' [110/253] Generating 'sklearn/tree/_partitioner.cpython-314-powerpc64le-linux-gnu.so.p/_partitioner.c' [111/253] Generating 'sklearn/tree/_criterion.cpython-314-powerpc64le-linux-gnu.so.p/_criterion.c' [112/253] Generating 'sklearn/tree/_utils.cpython-314-powerpc64le-linux-gnu.so.p/_utils.c' [113/253] Compiling C object sklearn/__check_build/_check_build.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__check_build.c.o [114/253] Compiling C object sklearn/_isotonic.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__isotonic.c.o [115/253] Compiling C object sklearn/_cyutility.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated_..__cyutility.c.o [116/253] Compiling C object sklearn/utils/_cython_blas.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__cython_blas.c.o [117/253] Compiling C object sklearn/utils/arrayfuncs.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated_arrayfuncs.c.o [118/253] Compiling C object sklearn/utils/murmurhash.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated_murmurhash.c.o [119/253] Compiling C++ object sklearn/utils/murmurhash.cpython-314-powerpc64le-linux-gnu.so.p/src_MurmurHash3.cpp.o [120/253] Compiling C++ object sklearn/utils/_fast_dict.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__fast_dict.cpp.o [121/253] Compiling C object sklearn/utils/_openmp_helpers.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__openmp_helpers.c.o [122/253] Compiling C object sklearn/utils/sparsefuncs_fast.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated_sparsefuncs_fast.c.o [123/253] Compiling C object sklearn/utils/_random.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__random.c.o [124/253] Compiling C object sklearn/utils/_heap.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__heap.c.o [125/253] Compiling C object sklearn/utils/_typedefs.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__typedefs.c.o [126/253] Compiling C object sklearn/utils/_sorting.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__sorting.c.o [127/253] Compiling C object sklearn/utils/_isfinite.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__isfinite.c.o [128/253] Compiling C++ object sklearn/utils/_vector_sentinel.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__vector_sentinel.cpp.o [129/253] Compiling C object sklearn/utils/_seq_dataset.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__seq_dataset.c.o [130/253] Compiling C object sklearn/utils/_weight_vector.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__weight_vector.c.o [131/253] Compiling C object sklearn/metrics/_pairwise_fast.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__pairwise_fast.c.o [132/253] Compiling C++ object sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__datasets_pair.cpp.o [133/253] Compiling C object sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__loss.c.o In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_28loss_gradient’, inlined from ‘__pyx_fuse_0_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_29loss_gradient’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:87046:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:87135:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 87135 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_1, __pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_0_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_29loss_gradient’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:87068:11: note: ‘__pyx_v_p’ was declared here 87068 | double *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_30loss_gradient’, inlined from ‘__pyx_fuse_0_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_31loss_gradient’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:87662:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:87751:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 87751 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_1, __pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_0_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_31loss_gradient’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:87684:11: note: ‘__pyx_v_p’ was declared here 87684 | double *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_32loss_gradient’, inlined from ‘__pyx_fuse_1_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_33loss_gradient’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:88278:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:88367:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 88367 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_1, __pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_1_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_33loss_gradient’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:88300:10: note: ‘__pyx_v_p’ was declared here 88300 | float *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_34loss_gradient’, inlined from ‘__pyx_fuse_1_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_35loss_gradient’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:88894:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:88983:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 88983 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_1, __pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_1_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_35loss_gradient’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:88916:10: note: ‘__pyx_v_p’ was declared here 88916 | float *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_48gradient_hessian’, inlined from ‘__pyx_fuse_0_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_49gradient_hessian’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:92156:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:92244:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 92244 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_0_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_49gradient_hessian’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:92177:11: note: ‘__pyx_v_p’ was declared here 92177 | double *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_50gradient_hessian’, inlined from ‘__pyx_fuse_0_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_51gradient_hessian’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:92679:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:92767:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 92767 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_0_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_51gradient_hessian’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:92700:11: note: ‘__pyx_v_p’ was declared here 92700 | double *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_52gradient_hessian’, inlined from ‘__pyx_fuse_1_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_53gradient_hessian’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:93202:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:93290:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 93290 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_1_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_53gradient_hessian’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:93223:10: note: ‘__pyx_v_p’ was declared here 93223 | float *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_54gradient_hessian’, inlined from ‘__pyx_fuse_1_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_55gradient_hessian’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:93725:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:93813:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 93813 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_1_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_55gradient_hessian’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:93746:10: note: ‘__pyx_v_p’ was declared here 93746 | float *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_58gradient_proba’, inlined from ‘__pyx_fuse_0_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_59gradient_proba’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:94591:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:94680:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 94680 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_0_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_59gradient_proba’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:94612:11: note: ‘__pyx_v_p’ was declared here 94612 | double *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_60gradient_proba’, inlined from ‘__pyx_fuse_0_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_61gradient_proba’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:95096:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:95185:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 95185 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_0_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_61gradient_proba’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:95117:11: note: ‘__pyx_v_p’ was declared here 95117 | double *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_62gradient_proba’, inlined from ‘__pyx_fuse_1_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_63gradient_proba’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:95601:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:95690:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 95690 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_1_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_63gradient_proba’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:95622:10: note: ‘__pyx_v_p’ was declared here 95622 | float *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_64gradient_proba’, inlined from ‘__pyx_fuse_1_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_65gradient_proba’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:96106:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:96195:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 96195 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_1_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_65gradient_proba’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:96127:10: note: ‘__pyx_v_p’ was declared here 96127 | float *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_44gradient’, inlined from ‘__pyx_fuse_1_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_45gradient’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:91314:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:91401:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 91401 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_1_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_45gradient’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:91334:10: note: ‘__pyx_v_p’ was declared here 91334 | float *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_42gradient’, inlined from ‘__pyx_fuse_1_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_43gradient’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:90824:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:90911:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 90911 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_1_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_43gradient’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:90844:10: note: ‘__pyx_v_p’ was declared here 90844 | float *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_40gradient’, inlined from ‘__pyx_fuse_0_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_41gradient’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:90334:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:90421:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 90421 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_0_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_41gradient’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:90354:11: note: ‘__pyx_v_p’ was declared here 90354 | double *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_38gradient’, inlined from ‘__pyx_fuse_0_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_39gradient’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:89844:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:89931:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 89931 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_0_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_39gradient’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:89864:11: note: ‘__pyx_v_p’ was declared here 89864 | double *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_24loss’, inlined from ‘__pyx_fuse_1_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_25loss’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:86184:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:86267:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 86267 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_1_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_25loss’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:86205:10: note: ‘__pyx_v_p’ was declared here 86205 | float *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_22loss’, inlined from ‘__pyx_fuse_1_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_23loss’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:85674:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:85757:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 85757 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_1_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_23loss’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:85695:10: note: ‘__pyx_v_p’ was declared here 85695 | float *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_20loss’, inlined from ‘__pyx_fuse_0_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_21loss’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:85164:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:85247:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 85247 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_0_1__pyx_pw_5_loss_21CyHalfMultinomialLoss_21loss’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:85185:11: note: ‘__pyx_v_p’ was declared here 85185 | double *__pyx_v_p; | ^~~~~~~~~ In function ‘__pyx_pf_5_loss_21CyHalfMultinomialLoss_18loss’, inlined from ‘__pyx_fuse_0_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_19loss’ at sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:84654:13: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:84737:23: warning: ‘__pyx_v_p’ may be used uninitialized [-Wmaybe-uninitialized] 84737 | #pragma omp parallel firstprivate(__pyx_v_p) private(__pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8) num_threads(__pyx_v_n_threads) | ^~~ sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c: In function ‘__pyx_fuse_0_0__pyx_pw_5_loss_21CyHalfMultinomialLoss_19loss’: sklearn/_loss/_loss.cpython-314-powerpc64le-linux-gnu.so.p/_loss.c:84675:11: note: ‘__pyx_v_p’ was declared here 84675 | double *__pyx_v_p; | ^~~~~~~~~ [134/253] Compiling C object sklearn/metrics/_dist_metrics.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__dist_metrics.c.o [135/253] Compiling C++ object sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__base.cpp.o sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp: In function ‘__pyx_f_7sklearn_7metrics_29_pairwise_distances_reduction_5_base_24BaseDistancesReduction32__parallel_on_X(__pyx_obj_7sklearn_7metrics_29_pairwise_distances_reduction_5_base_BaseDistancesReduction32*)’: sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp:9020:21: warning: ‘__pyx_v_thread_num’ is used uninitialized [-Wuninitialized] 9020 | #pragma omp parallel firstprivate(__pyx_v_thread_num) private(__pyx_t_1, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7) num_threads(__pyx_v_self->chunks_n_threads) | ^~~ sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp:8991:45: note: ‘__pyx_v_thread_num’ was declared here 8991 | __pyx_t_7sklearn_5utils_9_typedefs_intp_t __pyx_v_thread_num; | ^~~~~~~~~~~~~~~~~~ sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp: In function ‘__pyx_f_7sklearn_7metrics_29_pairwise_distances_reduction_5_base_24BaseDistancesReduction64__parallel_on_X(__pyx_obj_7sklearn_7metrics_29_pairwise_distances_reduction_5_base_BaseDistancesReduction64*)’: sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp:6192:21: warning: ‘__pyx_v_thread_num’ is used uninitialized [-Wuninitialized] 6192 | #pragma omp parallel firstprivate(__pyx_v_thread_num) private(__pyx_t_1, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7) num_threads(__pyx_v_self->chunks_n_threads) | ^~~ sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp:6163:45: note: ‘__pyx_v_thread_num’ was declared here 6163 | __pyx_t_7sklearn_5utils_9_typedefs_intp_t __pyx_v_thread_num; | ^~~~~~~~~~~~~~~~~~ sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp: In function ‘__pyx_f_7sklearn_7metrics_29_pairwise_distances_reduction_5_base_24BaseDistancesReduction32__parallel_on_Y(__pyx_obj_7sklearn_7metrics_29_pairwise_distances_reduction_5_base_BaseDistancesReduction32*)’: sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp:9385:23: warning: ‘__pyx_v_thread_num’ may be used uninitialized [-Wmaybe-uninitialized] 9385 | #pragma omp parallel firstprivate(__pyx_v_thread_num) private(__pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7) num_threads(__pyx_v_self->chunks_n_threads) | ^~~ sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp:9285:45: note: ‘__pyx_v_thread_num’ was declared here 9285 | __pyx_t_7sklearn_5utils_9_typedefs_intp_t __pyx_v_thread_num; | ^~~~~~~~~~~~~~~~~~ sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp: In function ‘__pyx_f_7sklearn_7metrics_29_pairwise_distances_reduction_5_base_24BaseDistancesReduction64__parallel_on_Y(__pyx_obj_7sklearn_7metrics_29_pairwise_distances_reduction_5_base_BaseDistancesReduction64*)’: sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp:6557:23: warning: ‘__pyx_v_thread_num’ may be used uninitialized [-Wmaybe-uninitialized] 6557 | #pragma omp parallel firstprivate(__pyx_v_thread_num) private(__pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7) num_threads(__pyx_v_self->chunks_n_threads) | ^~~ sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp:6457:45: note: ‘__pyx_v_thread_num’ was declared here 6457 | __pyx_t_7sklearn_5utils_9_typedefs_intp_t __pyx_v_thread_num; | ^~~~~~~~~~~~~~~~~~ In function ‘__pyx_f_7sklearn_7metrics_29_pairwise_distances_reduction_5_base__sqeuclidean_row_norms32_dense(__Pyx_memviewslice, long)’, inlined from ‘__pyx_f_7sklearn_7metrics_29_pairwise_distances_reduction_5_base__sqeuclidean_row_norms32(_object*, long, int)’ at sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp:7974:113: sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp:4564:21: warning: ‘__pyx_v_thread_num’ may be used uninitialized [-Wmaybe-uninitialized] 4564 | #pragma omp parallel firstprivate(__pyx_v_thread_num) private(__pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_17, __pyx_t_18) num_threads(__pyx_v_num_threads) | ^~~ sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp: In function ‘__pyx_f_7sklearn_7metrics_29_pairwise_distances_reduction_5_base__sqeuclidean_row_norms32(_object*, long, int)’: sklearn/metrics/_pairwise_distances_reduction/_base.cpython-314-powerpc64le-linux-gnu.so.p/_base.cpp:4394:45: note: ‘__pyx_v_thread_num’ was declared here 4394 | __pyx_t_7sklearn_5utils_9_typedefs_intp_t __pyx_v_thread_num; | ^~~~~~~~~~~~~~~~~~ [136/253] Compiling C++ object sklearn/metrics/_pairwise_distances_reduction/_argkmin.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__argkmin.cpp.o sklearn/metrics/_pairwise_distances_reduction/_argkmin.cpython-314-powerpc64le-linux-gnu.so.p/_argkmin.cpp: In function ‘__pyx_f_7sklearn_7metrics_29_pairwise_distances_reduction_8_argkmin_9ArgKmin64_compute_exact_distances(__pyx_obj_7sklearn_7metrics_29_pairwise_distances_reduction_8_argkmin_ArgKmin64*) [clone ._omp_fn.0]’: sklearn/metrics/_pairwise_distances_reduction/_argkmin.cpython-314-powerpc64le-linux-gnu.so.p/_argkmin.cpp:6701:138: warning: ‘__pyx_clineno’ may be used uninitialized [-Wmaybe-uninitialized] 6701 | __pyx_parallel_filename = __pyx_filename; __pyx_parallel_lineno = __pyx_lineno; __pyx_parallel_clineno = __pyx_clineno; | ~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~ sklearn/metrics/_pairwise_distances_reduction/_argkmin.cpython-314-powerpc64le-linux-gnu.so.p/_argkmin.cpp:6576:7: note: ‘__pyx_clineno’ was declared here 6576 | int __pyx_clineno = 0; | ^~~~~~~~~~~~~ sklearn/metrics/_pairwise_distances_reduction/_argkmin.cpython-314-powerpc64le-linux-gnu.so.p/_argkmin.cpp: In function ‘__pyx_f_7sklearn_7metrics_29_pairwise_distances_reduction_8_argkmin_9ArgKmin32_compute_exact_distances(__pyx_obj_7sklearn_7metrics_29_pairwise_distances_reduction_8_argkmin_ArgKmin32*) [clone ._omp_fn.0]’: sklearn/metrics/_pairwise_distances_reduction/_argkmin.cpython-314-powerpc64le-linux-gnu.so.p/_argkmin.cpp:11125:138: warning: ‘__pyx_clineno’ may be used uninitialized [-Wmaybe-uninitialized] 11125 | __pyx_parallel_filename = __pyx_filename; __pyx_parallel_lineno = __pyx_lineno; __pyx_parallel_clineno = __pyx_clineno; | ~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~ sklearn/metrics/_pairwise_distances_reduction/_argkmin.cpython-314-powerpc64le-linux-gnu.so.p/_argkmin.cpp:11000:7: note: ‘__pyx_clineno’ was declared here 11000 | int __pyx_clineno = 0; | ^~~~~~~~~~~~~ [137/253] Compiling C++ object sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__middle_term_computer.cpp.o [138/253] Compiling C++ object sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__radius_neighbors.cpp.o sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.cpython-314-powerpc64le-linux-gnu.so.p/_radius_neighbors.cpp: In function ‘__pyx_f_7sklearn_7metrics_29_pairwise_distances_reduction_17_radius_neighbors_17RadiusNeighbors64_compute_exact_distances(__pyx_obj_7sklearn_7metrics_29_pairwise_distances_reduction_17_radius_neighbors_RadiusNeighbors64*) [clone ._omp_fn.0]’: sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.cpython-314-powerpc64le-linux-gnu.so.p/_radius_neighbors.cpp:9305:138: warning: ‘__pyx_clineno’ may be used uninitialized [-Wmaybe-uninitialized] 9305 | __pyx_parallel_filename = __pyx_filename; __pyx_parallel_lineno = __pyx_lineno; __pyx_parallel_clineno = __pyx_clineno; | ~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~ sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.cpython-314-powerpc64le-linux-gnu.so.p/_radius_neighbors.cpp:9189:7: note: ‘__pyx_clineno’ was declared here 9189 | int __pyx_clineno = 0; | ^~~~~~~~~~~~~ sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.cpython-314-powerpc64le-linux-gnu.so.p/_radius_neighbors.cpp: In function ‘__pyx_f_7sklearn_7metrics_29_pairwise_distances_reduction_17_radius_neighbors_17RadiusNeighbors32_compute_exact_distances(__pyx_obj_7sklearn_7metrics_29_pairwise_distances_reduction_17_radius_neighbors_RadiusNeighbors32*) [clone ._omp_fn.0]’: sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.cpython-314-powerpc64le-linux-gnu.so.p/_radius_neighbors.cpp:13539:138: warning: ‘__pyx_clineno’ may be used uninitialized [-Wmaybe-uninitialized] 13539 | __pyx_parallel_filename = __pyx_filename; __pyx_parallel_lineno = __pyx_lineno; __pyx_parallel_clineno = __pyx_clineno; | ~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~ sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.cpython-314-powerpc64le-linux-gnu.so.p/_radius_neighbors.cpp:13423:7: note: ‘__pyx_clineno’ was declared here 13423 | int __pyx_clineno = 0; | ^~~~~~~~~~~~~ [139/253] Compiling C++ object sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__argkmin_classmode.cpp.o [140/253] Compiling C++ object sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__radius_neighbors_classmode.cpp.o [141/253] Compiling C++ object sklearn/cluster/_dbscan_inner.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__dbscan_inner.cpp.o [142/253] Compiling C object sklearn/metrics/cluster/_expected_mutual_info_fast.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__expected_mutual_info_fast.c.o [143/253] Compiling C++ object sklearn/cluster/_hierarchical_fast.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__hierarchical_fast.cpp.o [144/253] Compiling C object sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__k_means_lloyd.c.o In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_6lloyd_iter_chunked_dense’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_7lloyd_iter_chunked_dense’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6145:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6460:21: warning: ‘__pyx_v_weight_in_clusters_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 6460 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_pairwise_distances_chunk, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_16, __pyx_t_19, __pyx_t_2, __pyx_t_20, __pyx_t_6) firstprivate(__pyx_t_15, __pyx_t_17, __pyx_t_18) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_7lloyd_iter_chunked_dense’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6184:11: note: ‘__pyx_v_weight_in_clusters_chunk’ was declared here 6184 | double *__pyx_v_weight_in_clusters_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_6lloyd_iter_chunked_dense’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_7lloyd_iter_chunked_dense’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6145:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6460:21: warning: ‘__pyx_v_pairwise_distances_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 6460 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_pairwise_distances_chunk, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_16, __pyx_t_19, __pyx_t_2, __pyx_t_20, __pyx_t_6) firstprivate(__pyx_t_15, __pyx_t_17, __pyx_t_18) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_7lloyd_iter_chunked_dense’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6185:11: note: ‘__pyx_v_pairwise_distances_chunk’ was declared here 6185 | double *__pyx_v_pairwise_distances_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_6lloyd_iter_chunked_dense’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_7lloyd_iter_chunked_dense’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6145:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6460:21: warning: ‘__pyx_v_k’ may be used uninitialized [-Wmaybe-uninitialized] 6460 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_pairwise_distances_chunk, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_16, __pyx_t_19, __pyx_t_2, __pyx_t_20, __pyx_t_6) firstprivate(__pyx_t_15, __pyx_t_17, __pyx_t_18) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_7lloyd_iter_chunked_dense’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6181:7: note: ‘__pyx_v_k’ was declared here 6181 | int __pyx_v_k; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_6lloyd_iter_chunked_dense’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_7lloyd_iter_chunked_dense’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6145:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6460:21: warning: ‘__pyx_v_j’ may be used uninitialized [-Wmaybe-uninitialized] 6460 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_pairwise_distances_chunk, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_16, __pyx_t_19, __pyx_t_2, __pyx_t_20, __pyx_t_6) firstprivate(__pyx_t_15, __pyx_t_17, __pyx_t_18) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_7lloyd_iter_chunked_dense’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6180:7: note: ‘__pyx_v_j’ was declared here 6180 | int __pyx_v_j; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_6lloyd_iter_chunked_dense’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_7lloyd_iter_chunked_dense’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6145:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6460:21: warning: ‘__pyx_v_centers_new_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 6460 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_pairwise_distances_chunk, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_16, __pyx_t_19, __pyx_t_2, __pyx_t_20, __pyx_t_6) firstprivate(__pyx_t_15, __pyx_t_17, __pyx_t_18) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_7lloyd_iter_chunked_dense’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:6183:11: note: ‘__pyx_v_centers_new_chunk’ was declared here 6183 | double *__pyx_v_centers_new_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_4lloyd_iter_chunked_dense’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_5lloyd_iter_chunked_dense’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5116:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5431:21: warning: ‘__pyx_v_weight_in_clusters_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 5431 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_pairwise_distances_chunk, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_16, __pyx_t_19, __pyx_t_2, __pyx_t_20, __pyx_t_6) firstprivate(__pyx_t_15, __pyx_t_17, __pyx_t_18) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_5lloyd_iter_chunked_dense’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5155:10: note: ‘__pyx_v_weight_in_clusters_chunk’ was declared here 5155 | float *__pyx_v_weight_in_clusters_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_4lloyd_iter_chunked_dense’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_5lloyd_iter_chunked_dense’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5116:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5431:21: warning: ‘__pyx_v_pairwise_distances_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 5431 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_pairwise_distances_chunk, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_16, __pyx_t_19, __pyx_t_2, __pyx_t_20, __pyx_t_6) firstprivate(__pyx_t_15, __pyx_t_17, __pyx_t_18) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_5lloyd_iter_chunked_dense’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5156:10: note: ‘__pyx_v_pairwise_distances_chunk’ was declared here 5156 | float *__pyx_v_pairwise_distances_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_4lloyd_iter_chunked_dense’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_5lloyd_iter_chunked_dense’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5116:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5431:21: warning: ‘__pyx_v_k’ may be used uninitialized [-Wmaybe-uninitialized] 5431 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_pairwise_distances_chunk, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_16, __pyx_t_19, __pyx_t_2, __pyx_t_20, __pyx_t_6) firstprivate(__pyx_t_15, __pyx_t_17, __pyx_t_18) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_5lloyd_iter_chunked_dense’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5152:7: note: ‘__pyx_v_k’ was declared here 5152 | int __pyx_v_k; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_4lloyd_iter_chunked_dense’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_5lloyd_iter_chunked_dense’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5116:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5431:21: warning: ‘__pyx_v_j’ may be used uninitialized [-Wmaybe-uninitialized] 5431 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_pairwise_distances_chunk, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_16, __pyx_t_19, __pyx_t_2, __pyx_t_20, __pyx_t_6) firstprivate(__pyx_t_15, __pyx_t_17, __pyx_t_18) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_5lloyd_iter_chunked_dense’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5151:7: note: ‘__pyx_v_j’ was declared here 5151 | int __pyx_v_j; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_4lloyd_iter_chunked_dense’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_5lloyd_iter_chunked_dense’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5116:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5431:21: warning: ‘__pyx_v_centers_new_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 5431 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_pairwise_distances_chunk, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_16, __pyx_t_19, __pyx_t_2, __pyx_t_20, __pyx_t_6) firstprivate(__pyx_t_15, __pyx_t_17, __pyx_t_18) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_5lloyd_iter_chunked_dense’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:5154:10: note: ‘__pyx_v_centers_new_chunk’ was declared here 5154 | float *__pyx_v_centers_new_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_12lloyd_iter_chunked_sparse’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_13lloyd_iter_chunked_sparse’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:9141:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:9527:21: warning: ‘__pyx_v_weight_in_clusters_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 9527 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_20, __pyx_t_21, __pyx_t_3, __pyx_t_4, __pyx_t_6) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_7, __pyx_t_8) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_13lloyd_iter_chunked_sparse’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:9182:11: note: ‘__pyx_v_weight_in_clusters_chunk’ was declared here 9182 | double *__pyx_v_weight_in_clusters_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_12lloyd_iter_chunked_sparse’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_13lloyd_iter_chunked_sparse’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:9141:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:9527:21: warning: ‘__pyx_v_k’ may be used uninitialized [-Wmaybe-uninitialized] 9527 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_20, __pyx_t_21, __pyx_t_3, __pyx_t_4, __pyx_t_6) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_7, __pyx_t_8) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_13lloyd_iter_chunked_sparse’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:9176:7: note: ‘__pyx_v_k’ was declared here 9176 | int __pyx_v_k; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_12lloyd_iter_chunked_sparse’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_13lloyd_iter_chunked_sparse’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:9141:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:9527:21: warning: ‘__pyx_v_j’ may be used uninitialized [-Wmaybe-uninitialized] 9527 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_20, __pyx_t_21, __pyx_t_3, __pyx_t_4, __pyx_t_6) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_7, __pyx_t_8) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_13lloyd_iter_chunked_sparse’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:9175:7: note: ‘__pyx_v_j’ was declared here 9175 | int __pyx_v_j; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_12lloyd_iter_chunked_sparse’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_13lloyd_iter_chunked_sparse’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:9141:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:9527:21: warning: ‘__pyx_v_centers_new_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 9527 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_20, __pyx_t_21, __pyx_t_3, __pyx_t_4, __pyx_t_6) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_7, __pyx_t_8) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_13lloyd_iter_chunked_sparse’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:9181:11: note: ‘__pyx_v_centers_new_chunk’ was declared here 9181 | double *__pyx_v_centers_new_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_10lloyd_iter_chunked_sparse’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_11lloyd_iter_chunked_sparse’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:7990:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:8376:21: warning: ‘__pyx_v_weight_in_clusters_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 8376 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_20, __pyx_t_21, __pyx_t_3, __pyx_t_4, __pyx_t_6) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_7, __pyx_t_8) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_11lloyd_iter_chunked_sparse’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:8031:10: note: ‘__pyx_v_weight_in_clusters_chunk’ was declared here 8031 | float *__pyx_v_weight_in_clusters_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_10lloyd_iter_chunked_sparse’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_11lloyd_iter_chunked_sparse’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:7990:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:8376:21: warning: ‘__pyx_v_k’ may be used uninitialized [-Wmaybe-uninitialized] 8376 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_20, __pyx_t_21, __pyx_t_3, __pyx_t_4, __pyx_t_6) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_7, __pyx_t_8) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_11lloyd_iter_chunked_sparse’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:8025:7: note: ‘__pyx_v_k’ was declared here 8025 | int __pyx_v_k; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_10lloyd_iter_chunked_sparse’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_11lloyd_iter_chunked_sparse’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:7990:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:8376:21: warning: ‘__pyx_v_j’ may be used uninitialized [-Wmaybe-uninitialized] 8376 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_20, __pyx_t_21, __pyx_t_3, __pyx_t_4, __pyx_t_6) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_7, __pyx_t_8) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_11lloyd_iter_chunked_sparse’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:8024:7: note: ‘__pyx_v_j’ was declared here 8024 | int __pyx_v_j; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_lloyd_10lloyd_iter_chunked_sparse’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_11lloyd_iter_chunked_sparse’ at sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:7990:13: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:8376:21: warning: ‘__pyx_v_centers_new_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 8376 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_20, __pyx_t_21, __pyx_t_3, __pyx_t_4, __pyx_t_6) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_7, __pyx_t_8) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_lloyd_11lloyd_iter_chunked_sparse’: sklearn/cluster/_k_means_lloyd.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_lloyd.c:8030:10: note: ‘__pyx_v_centers_new_chunk’ was declared here 8030 | float *__pyx_v_centers_new_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~ [145/253] Compiling C object sklearn/cluster/_k_means_common.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__k_means_common.c.o sklearn/cluster/_k_means_common.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_common.c: In function ‘__pyx_fuse_1__pyx_f_7sklearn_7cluster_15_k_means_common__inertia_sparse._omp_fn.0’: sklearn/cluster/_k_means_common.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_common.c:8745:138: warning: ‘__pyx_clineno’ may be used uninitialized [-Wmaybe-uninitialized] 8745 | __pyx_parallel_filename = __pyx_filename; __pyx_parallel_lineno = __pyx_lineno; __pyx_parallel_clineno = __pyx_clineno; | ~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~ sklearn/cluster/_k_means_common.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_common.c:8412:7: note: ‘__pyx_clineno’ was declared here 8412 | int __pyx_clineno = 0; | ^~~~~~~~~~~~~ sklearn/cluster/_k_means_common.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_common.c: In function ‘__pyx_fuse_0__pyx_f_7sklearn_7cluster_15_k_means_common__inertia_sparse._omp_fn.0’: sklearn/cluster/_k_means_common.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_common.c:8095:138: warning: ‘__pyx_clineno’ may be used uninitialized [-Wmaybe-uninitialized] 8095 | __pyx_parallel_filename = __pyx_filename; __pyx_parallel_lineno = __pyx_lineno; __pyx_parallel_clineno = __pyx_clineno; | ~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~ sklearn/cluster/_k_means_common.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_common.c:7762:7: note: ‘__pyx_clineno’ was declared here 7762 | int __pyx_clineno = 0; | ^~~~~~~~~~~~~ [146/253] Compiling C object sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__k_means_minibatch.c.o sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c: In function ‘__pyx_pf_7sklearn_7cluster_18_k_means_minibatch_6_minibatch_update_dense’: sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c:5302:21: warning: ‘__pyx_v_indices’ is used uninitialized [-Wuninitialized] 5302 | #pragma omp parallel firstprivate(__pyx_v_indices) private(__pyx_t_1, __pyx_t_2, __pyx_t_3) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c:5256:8: note: ‘__pyx_v_indices’ was declared here 5256 | int *__pyx_v_indices; | ^~~~~~~~~~~~~~~ sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c: In function ‘__pyx_pf_7sklearn_7cluster_18_k_means_minibatch_4_minibatch_update_dense’: sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c:5028:21: warning: ‘__pyx_v_indices’ is used uninitialized [-Wuninitialized] 5028 | #pragma omp parallel firstprivate(__pyx_v_indices) private(__pyx_t_1, __pyx_t_2, __pyx_t_3) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c:4982:8: note: ‘__pyx_v_indices’ was declared here 4982 | int *__pyx_v_indices; | ^~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_18_k_means_minibatch_12_minibatch_update_sparse’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_18_k_means_minibatch_13_minibatch_update_sparse’ at sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c:6742:13: sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c:6869:21: warning: ‘__pyx_v_indices’ may be used uninitialized [-Wmaybe-uninitialized] 6869 | #pragma omp parallel firstprivate(__pyx_v_indices) private(__pyx_t_5, __pyx_t_6, __pyx_t_7) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_18_k_means_minibatch_13_minibatch_update_sparse’: sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c:6764:8: note: ‘__pyx_v_indices’ was declared here 6764 | int *__pyx_v_indices; | ^~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_18_k_means_minibatch_10_minibatch_update_sparse’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_18_k_means_minibatch_11_minibatch_update_sparse’ at sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c:6396:13: sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c:6523:21: warning: ‘__pyx_v_indices’ may be used uninitialized [-Wmaybe-uninitialized] 6523 | #pragma omp parallel firstprivate(__pyx_v_indices) private(__pyx_t_5, __pyx_t_6, __pyx_t_7) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_18_k_means_minibatch_11_minibatch_update_sparse’: sklearn/cluster/_k_means_minibatch.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_minibatch.c:6418:8: note: ‘__pyx_v_indices’ was declared here 6418 | int *__pyx_v_indices; | ^~~~~~~~~~~~~~~ [147/253] Compiling C object sklearn/cluster/_hdbscan/_linkage.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__linkage.c.o [148/253] Compiling C object sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__k_means_elkan.c.o sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_14init_bounds_sparse._omp_fn.0’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:6754:138: warning: ‘__pyx_clineno’ may be used uninitialized [-Wmaybe-uninitialized] 6754 | __pyx_parallel_filename = __pyx_filename; __pyx_parallel_lineno = __pyx_lineno; __pyx_parallel_clineno = __pyx_clineno; | ~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:6253:7: note: ‘__pyx_clineno’ was declared here 6253 | int __pyx_clineno = 0; | ^~~~~~~~~~~~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_16init_bounds_sparse._omp_fn.0’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:7538:138: warning: ‘__pyx_clineno’ may be used uninitialized [-Wmaybe-uninitialized] 7538 | __pyx_parallel_filename = __pyx_filename; __pyx_parallel_lineno = __pyx_lineno; __pyx_parallel_clineno = __pyx_clineno; | ~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:7037:7: note: ‘__pyx_clineno’ was declared here 7037 | int __pyx_clineno = 0; | ^~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_22elkan_iter_chunked_dense’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_23elkan_iter_chunked_dense’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:9261:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:9535:21: warning: ‘__pyx_v_weight_in_clusters_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 9535 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_10, __pyx_t_12, __pyx_t_17, __pyx_t_18, __pyx_t_2, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) firstprivate(__pyx_t_11, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_23elkan_iter_chunked_dense’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:9304:11: note: ‘__pyx_v_weight_in_clusters_chunk’ was declared here 9304 | double *__pyx_v_weight_in_clusters_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_22elkan_iter_chunked_dense’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_23elkan_iter_chunked_dense’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:9261:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:9535:21: warning: ‘__pyx_v_k’ may be used uninitialized [-Wmaybe-uninitialized] 9535 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_10, __pyx_t_12, __pyx_t_17, __pyx_t_18, __pyx_t_2, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) firstprivate(__pyx_t_11, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_23elkan_iter_chunked_dense’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:9302:7: note: ‘__pyx_v_k’ was declared here 9302 | int __pyx_v_k; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_22elkan_iter_chunked_dense’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_23elkan_iter_chunked_dense’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:9261:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:9535:21: warning: ‘__pyx_v_j’ may be used uninitialized [-Wmaybe-uninitialized] 9535 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_10, __pyx_t_12, __pyx_t_17, __pyx_t_18, __pyx_t_2, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) firstprivate(__pyx_t_11, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_23elkan_iter_chunked_dense’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:9301:7: note: ‘__pyx_v_j’ was declared here 9301 | int __pyx_v_j; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_22elkan_iter_chunked_dense’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_23elkan_iter_chunked_dense’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:9261:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:9535:21: warning: ‘__pyx_v_centers_new_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 9535 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_10, __pyx_t_12, __pyx_t_17, __pyx_t_18, __pyx_t_2, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) firstprivate(__pyx_t_11, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_23elkan_iter_chunked_dense’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:9303:11: note: ‘__pyx_v_centers_new_chunk’ was declared here 9303 | double *__pyx_v_centers_new_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_20elkan_iter_chunked_dense’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_21elkan_iter_chunked_dense’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:8105:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:8379:21: warning: ‘__pyx_v_weight_in_clusters_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 8379 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_10, __pyx_t_12, __pyx_t_17, __pyx_t_18, __pyx_t_2, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) firstprivate(__pyx_t_11, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_21elkan_iter_chunked_dense’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:8148:10: note: ‘__pyx_v_weight_in_clusters_chunk’ was declared here 8148 | float *__pyx_v_weight_in_clusters_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_20elkan_iter_chunked_dense’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_21elkan_iter_chunked_dense’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:8105:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:8379:21: warning: ‘__pyx_v_k’ may be used uninitialized [-Wmaybe-uninitialized] 8379 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_10, __pyx_t_12, __pyx_t_17, __pyx_t_18, __pyx_t_2, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) firstprivate(__pyx_t_11, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_21elkan_iter_chunked_dense’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:8146:7: note: ‘__pyx_v_k’ was declared here 8146 | int __pyx_v_k; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_20elkan_iter_chunked_dense’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_21elkan_iter_chunked_dense’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:8105:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:8379:21: warning: ‘__pyx_v_j’ may be used uninitialized [-Wmaybe-uninitialized] 8379 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_10, __pyx_t_12, __pyx_t_17, __pyx_t_18, __pyx_t_2, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) firstprivate(__pyx_t_11, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_21elkan_iter_chunked_dense’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:8145:7: note: ‘__pyx_v_j’ was declared here 8145 | int __pyx_v_j; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_20elkan_iter_chunked_dense’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_21elkan_iter_chunked_dense’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:8105:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:8379:21: warning: ‘__pyx_v_centers_new_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 8379 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_1, __pyx_t_10, __pyx_t_12, __pyx_t_17, __pyx_t_18, __pyx_t_2, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) firstprivate(__pyx_t_11, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_21elkan_iter_chunked_dense’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:8147:10: note: ‘__pyx_v_centers_new_chunk’ was declared here 8147 | float *__pyx_v_centers_new_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_28elkan_iter_chunked_sparse’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_29elkan_iter_chunked_sparse’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:12926:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:13310:21: warning: ‘__pyx_v_weight_in_clusters_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 13310 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_22, __pyx_t_23, __pyx_t_3, __pyx_t_4, __pyx_t_8) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_20, __pyx_t_21, __pyx_t_5, __pyx_t_6) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_29elkan_iter_chunked_sparse’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:12972:11: note: ‘__pyx_v_weight_in_clusters_chunk’ was declared here 12972 | double *__pyx_v_weight_in_clusters_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_28elkan_iter_chunked_sparse’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_29elkan_iter_chunked_sparse’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:12926:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:13310:21: warning: ‘__pyx_v_k’ may be used uninitialized [-Wmaybe-uninitialized] 13310 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_22, __pyx_t_23, __pyx_t_3, __pyx_t_4, __pyx_t_8) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_20, __pyx_t_21, __pyx_t_5, __pyx_t_6) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_29elkan_iter_chunked_sparse’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:12969:7: note: ‘__pyx_v_k’ was declared here 12969 | int __pyx_v_k; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_28elkan_iter_chunked_sparse’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_29elkan_iter_chunked_sparse’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:12926:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:13310:21: warning: ‘__pyx_v_j’ may be used uninitialized [-Wmaybe-uninitialized] 13310 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_22, __pyx_t_23, __pyx_t_3, __pyx_t_4, __pyx_t_8) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_20, __pyx_t_21, __pyx_t_5, __pyx_t_6) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_29elkan_iter_chunked_sparse’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:12968:7: note: ‘__pyx_v_j’ was declared here 12968 | int __pyx_v_j; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_28elkan_iter_chunked_sparse’, inlined from ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_29elkan_iter_chunked_sparse’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:12926:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:13310:21: warning: ‘__pyx_v_centers_new_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 13310 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_22, __pyx_t_23, __pyx_t_3, __pyx_t_4, __pyx_t_8) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_20, __pyx_t_21, __pyx_t_5, __pyx_t_6) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_1__pyx_pw_7sklearn_7cluster_14_k_means_elkan_29elkan_iter_chunked_sparse’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:12971:11: note: ‘__pyx_v_centers_new_chunk’ was declared here 12971 | double *__pyx_v_centers_new_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_26elkan_iter_chunked_sparse’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_27elkan_iter_chunked_sparse’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:11587:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:11971:21: warning: ‘__pyx_v_weight_in_clusters_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 11971 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_22, __pyx_t_23, __pyx_t_3, __pyx_t_4, __pyx_t_8) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_20, __pyx_t_21, __pyx_t_5, __pyx_t_6) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_27elkan_iter_chunked_sparse’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:11633:10: note: ‘__pyx_v_weight_in_clusters_chunk’ was declared here 11633 | float *__pyx_v_weight_in_clusters_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_26elkan_iter_chunked_sparse’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_27elkan_iter_chunked_sparse’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:11587:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:11971:21: warning: ‘__pyx_v_k’ may be used uninitialized [-Wmaybe-uninitialized] 11971 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_22, __pyx_t_23, __pyx_t_3, __pyx_t_4, __pyx_t_8) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_20, __pyx_t_21, __pyx_t_5, __pyx_t_6) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_27elkan_iter_chunked_sparse’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:11630:7: note: ‘__pyx_v_k’ was declared here 11630 | int __pyx_v_k; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_26elkan_iter_chunked_sparse’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_27elkan_iter_chunked_sparse’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:11587:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:11971:21: warning: ‘__pyx_v_j’ may be used uninitialized [-Wmaybe-uninitialized] 11971 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_22, __pyx_t_23, __pyx_t_3, __pyx_t_4, __pyx_t_8) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_20, __pyx_t_21, __pyx_t_5, __pyx_t_6) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_27elkan_iter_chunked_sparse’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:11629:7: note: ‘__pyx_v_j’ was declared here 11629 | int __pyx_v_j; | ^~~~~~~~~ In function ‘__pyx_pf_7sklearn_7cluster_14_k_means_elkan_26elkan_iter_chunked_sparse’, inlined from ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_27elkan_iter_chunked_sparse’ at sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:11587:13: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:11971:21: warning: ‘__pyx_v_centers_new_chunk’ may be used uninitialized [-Wmaybe-uninitialized] 11971 | #pragma omp parallel firstprivate(__pyx_v_centers_new_chunk, __pyx_v_j, __pyx_v_k, __pyx_v_weight_in_clusters_chunk) private(__pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_22, __pyx_t_23, __pyx_t_3, __pyx_t_4, __pyx_t_8) firstprivate(__pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_20, __pyx_t_21, __pyx_t_5, __pyx_t_6) __Pyx_shared_in_cpython_freethreading(__pyx_parallel_freethreading_mutex) private(__pyx_filename, __pyx_lineno, __pyx_clineno) shared(__pyx_parallel_why, __pyx_parallel_exc_type, __pyx_parallel_exc_value, __pyx_parallel_exc_tb) num_threads(__pyx_v_n_threads) | ^~~ sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c: In function ‘__pyx_fuse_0__pyx_pw_7sklearn_7cluster_14_k_means_elkan_27elkan_iter_chunked_sparse’: sklearn/cluster/_k_means_elkan.cpython-314-powerpc64le-linux-gnu.so.p/_k_means_elkan.c:11632:10: note: ‘__pyx_v_centers_new_chunk’ was declared here 11632 | float *__pyx_v_centers_new_chunk; | ^~~~~~~~~~~~~~~~~~~~~~~~~ [149/253] Compiling C object sklearn/cluster/_hdbscan/_reachability.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__reachability.c.o [150/253] Compiling C object sklearn/decomposition/_online_lda_fast.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__online_lda_fast.c.o [151/253] Compiling C object sklearn/cluster/_hdbscan/_tree.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__tree.c.o [152/253] Compiling C object sklearn/ensemble/_gradient_boosting.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__gradient_boosting.c.o [153/253] Compiling C object sklearn/decomposition/_cdnmf_fast.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__cdnmf_fast.c.o [154/253] Compiling C object sklearn/ensemble/_hist_gradient_boosting/_gradient_boosting.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__gradient_boosting.c.o [155/253] Compiling C object sklearn/ensemble/_hist_gradient_boosting/histogram.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated_histogram.c.o [156/253] Compiling C object sklearn/ensemble/_hist_gradient_boosting/_binning.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__binning.c.o [157/253] Compiling C object sklearn/datasets/_svmlight_format_fast.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__svmlight_format_fast.c.o [158/253] Compiling C object sklearn/ensemble/_hist_gradient_boosting/splitting.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated_splitting.c.o [159/253] Compiling C object sklearn/ensemble/_hist_gradient_boosting/_bitset.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__bitset.c.o [160/253] Compiling C object sklearn/ensemble/_hist_gradient_boosting/common.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated_common.c.o [161/253] Compiling C object sklearn/ensemble/_hist_gradient_boosting/_predictor.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__predictor.c.o [162/253] Compiling C++ object sklearn/feature_extraction/_hashing_fast.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__hashing_fast.cpp.o [163/253] Compiling C object sklearn/linear_model/_sag_fast.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__sag_fast.c.o [164/253] Compiling C object sklearn/linear_model/_sgd_fast.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__sgd_fast.c.o [165/253] Compiling C object sklearn/manifold/_utils.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__utils.c.o [166/253] Compiling C object sklearn/manifold/_barnes_hut_tsne.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__barnes_hut_tsne.c.o sklearn/manifold/_barnes_hut_tsne.cpython-314-powerpc64le-linux-gnu.so.p/_barnes_hut_tsne.c: In function ‘__pyx_f_7sklearn_8manifold_16_barnes_hut_tsne_compute_gradient_negative’: sklearn/manifold/_barnes_hut_tsne.cpython-314-powerpc64le-linux-gnu.so.p/_barnes_hut_tsne.c:6286:21: warning: ‘__pyx_v_summary’ is used uninitialized [-Wuninitialized] 6286 | #pragma omp parallel firstprivate(__pyx_v_force, __pyx_v_neg_force, __pyx_v_pos, __pyx_v_summary) reduction(+:__pyx_v_dta) reduction(+:__pyx_v_dtb) reduction(+:__pyx_v_sum_Q) private(__pyx_t_1, __pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_num_threads) | ^~~ sklearn/manifold/_barnes_hut_tsne.cpython-314-powerpc64le-linux-gnu.so.p/_barnes_hut_tsne.c:6137:10: note: ‘__pyx_v_summary’ was declared here 6137 | float *__pyx_v_summary; | ^~~~~~~~~~~~~~~ sklearn/manifold/_barnes_hut_tsne.cpython-314-powerpc64le-linux-gnu.so.p/_barnes_hut_tsne.c:6286:21: warning: ‘__pyx_v_pos’ is used uninitialized [-Wuninitialized] 6286 | #pragma omp parallel firstprivate(__pyx_v_force, __pyx_v_neg_force, __pyx_v_pos, __pyx_v_summary) reduction(+:__pyx_v_dta) reduction(+:__pyx_v_dtb) reduction(+:__pyx_v_sum_Q) private(__pyx_t_1, __pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_num_threads) | ^~~ sklearn/manifold/_barnes_hut_tsne.cpython-314-powerpc64le-linux-gnu.so.p/_barnes_hut_tsne.c:6132:10: note: ‘__pyx_v_pos’ was declared here 6132 | float *__pyx_v_pos; | ^~~~~~~~~~~ sklearn/manifold/_barnes_hut_tsne.cpython-314-powerpc64le-linux-gnu.so.p/_barnes_hut_tsne.c:6286:21: warning: ‘__pyx_v_neg_force’ is used uninitialized [-Wuninitialized] 6286 | #pragma omp parallel firstprivate(__pyx_v_force, __pyx_v_neg_force, __pyx_v_pos, __pyx_v_summary) reduction(+:__pyx_v_dta) reduction(+:__pyx_v_dtb) reduction(+:__pyx_v_sum_Q) private(__pyx_t_1, __pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_num_threads) | ^~~ sklearn/manifold/_barnes_hut_tsne.cpython-314-powerpc64le-linux-gnu.so.p/_barnes_hut_tsne.c:6131:10: note: ‘__pyx_v_neg_force’ was declared here 6131 | float *__pyx_v_neg_force; | ^~~~~~~~~~~~~~~~~ sklearn/manifold/_barnes_hut_tsne.cpython-314-powerpc64le-linux-gnu.so.p/_barnes_hut_tsne.c:6286:21: warning: ‘__pyx_v_force’ is used uninitialized [-Wuninitialized] 6286 | #pragma omp parallel firstprivate(__pyx_v_force, __pyx_v_neg_force, __pyx_v_pos, __pyx_v_summary) reduction(+:__pyx_v_dta) reduction(+:__pyx_v_dtb) reduction(+:__pyx_v_sum_Q) private(__pyx_t_1, __pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_2, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_num_threads) | ^~~ sklearn/manifold/_barnes_hut_tsne.cpython-314-powerpc64le-linux-gnu.so.p/_barnes_hut_tsne.c:6130:10: note: ‘__pyx_v_force’ was declared here 6130 | float *__pyx_v_force; | ^~~~~~~~~~~~~ sklearn/manifold/_barnes_hut_tsne.cpython-314-powerpc64le-linux-gnu.so.p/_barnes_hut_tsne.c: In function ‘__pyx_f_7sklearn_8manifold_16_barnes_hut_tsne_compute_gradient_positive’: sklearn/manifold/_barnes_hut_tsne.cpython-314-powerpc64le-linux-gnu.so.p/_barnes_hut_tsne.c:5741:21: warning: ‘__pyx_v_buff’ is used uninitialized [-Wuninitialized] 5741 | #pragma omp parallel firstprivate(__pyx_v_buff) reduction(+:__pyx_v_C) private(__pyx_t_1, __pyx_t_10, __pyx_t_11, __pyx_t_12, __pyx_t_13, __pyx_t_14, __pyx_t_15, __pyx_t_16, __pyx_t_17, __pyx_t_18, __pyx_t_19, __pyx_t_2, __pyx_t_20, __pyx_t_21, __pyx_t_3, __pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8, __pyx_t_9) num_threads(__pyx_v_num_threads) | ^~~ sklearn/manifold/_barnes_hut_tsne.cpython-314-powerpc64le-linux-gnu.so.p/_barnes_hut_tsne.c:5620:10: note: ‘__pyx_v_buff’ was declared here 5620 | float *__pyx_v_buff; | ^~~~~~~~~~~~ [167/253] Compiling C object sklearn/linear_model/_cd_fast.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__cd_fast.c.o [168/253] Compiling C++ object sklearn/neighbors/_partition_nodes.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__partition_nodes.cpp.o [169/253] Compiling C object sklearn/neighbors/_quad_tree.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__quad_tree.c.o [170/253] Compiling C object sklearn/preprocessing/_csr_polynomial_expansion.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__csr_polynomial_expansion.c.o [171/253] Compiling C object sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__ball_tree.c.o sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree64__recursive_build’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:16525:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 16525 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_1)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree64__query_radius_single’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:23891:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 23891 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree64__kde_single_breadthfirst’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:24440:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 24440 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree64__kde_single_depthfirst’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:25345:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 25345 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree64__two_point_single’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:25955:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 25955 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree64__two_point_dual’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:26358:22: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 26358 | __pyx_v_idx_array1 = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_1)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:26368:22: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 26368 | __pyx_v_idx_array2 = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_other->idx_array.data) + __pyx_t_1)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree32__recursive_build’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:30400:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 30400 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_1)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree32__query_radius_single’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:37775:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 37775 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree32__kde_single_breadthfirst’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:38324:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 38324 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree32__kde_single_depthfirst’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:39229:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 39229 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree32__two_point_single’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:39839:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 39839 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree32__two_point_dual’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:40242:22: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 40242 | __pyx_v_idx_array1 = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_1)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:40252:22: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 40252 | __pyx_v_idx_array2 = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_other->idx_array.data) + __pyx_t_1)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_init_node64’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:42158:20: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 42158 | __pyx_v_centroid = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_float64_t const *) ( /* dim=2 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_float64_t const *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_tree->node_bounds.data + __pyx_t_2 * __pyx_v_tree->node_bounds.strides[0]) ) + __pyx_t_3 * __pyx_v_tree->node_bounds.strides[1]) )) + __pyx_t_1)) )))); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_init_node32’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:43531:20: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 43531 | __pyx_v_centroid = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_float32_t const *) ( /* dim=2 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_float32_t const *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_tree->node_bounds.data + __pyx_t_2 * __pyx_v_tree->node_bounds.strides[0]) ) + __pyx_t_3 * __pyx_v_tree->node_bounds.strides[1]) )) + __pyx_t_1)) )))); | ^ In function ‘__pyx_fuse_0__pyx_f_7sklearn_9neighbors_10_ball_tree__total_node_weight’, inlined from ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree32__kde_single_depthfirst’ at sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:39551:19: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:41868:52: warning: ‘__pyx_v_sample_weight’ may be used uninitialized [-Wmaybe-uninitialized] 41868 | __pyx_v_N = (__pyx_v_N + (__pyx_v_sample_weight[(__pyx_v_idx_array[__pyx_v_i])])); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree32__kde_single_depthfirst’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:39129:55: note: ‘__pyx_v_sample_weight’ was declared here 39129 | __pyx_t_7sklearn_5utils_9_typedefs_float32_t const *__pyx_v_sample_weight; | ^~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_fuse_1__pyx_f_7sklearn_9neighbors_10_ball_tree__total_node_weight’, inlined from ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree64__kde_single_depthfirst’ at sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:25667:19: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:41927:52: warning: ‘__pyx_v_sample_weight’ may be used uninitialized [-Wmaybe-uninitialized] 41927 | __pyx_v_N = (__pyx_v_N + (__pyx_v_sample_weight[(__pyx_v_idx_array[__pyx_v_i])])); | ^ sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_10_ball_tree_12BinaryTree64__kde_single_depthfirst’: sklearn/neighbors/_ball_tree.cpython-314-powerpc64le-linux-gnu.so.p/_ball_tree.c:25245:55: note: ‘__pyx_v_sample_weight’ was declared here 25245 | __pyx_t_7sklearn_5utils_9_typedefs_float64_t const *__pyx_v_sample_weight; | ^~~~~~~~~~~~~~~~~~~~~ [172/253] Compiling C object sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__kd_tree.c.o sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree64__recursive_build’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:16505:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 16505 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_1)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree64__query_radius_single’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:23871:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 23871 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree64__kde_single_breadthfirst’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:24420:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 24420 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree64__kde_single_depthfirst’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:25325:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 25325 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree64__two_point_single’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:25935:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 25935 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree64__two_point_dual’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:26338:22: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 26338 | __pyx_v_idx_array1 = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_1)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:26348:22: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 26348 | __pyx_v_idx_array2 = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_other->idx_array.data) + __pyx_t_1)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree32__recursive_build’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:30380:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 30380 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_1)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree32__query_radius_single’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:37755:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 37755 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree32__kde_single_breadthfirst’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:38304:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 38304 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree32__kde_single_depthfirst’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:39209:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 39209 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree32__two_point_single’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:39819:21: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 39819 | __pyx_v_idx_array = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_2)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree32__two_point_dual’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:40222:22: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 40222 | __pyx_v_idx_array1 = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_self->idx_array.data) + __pyx_t_1)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:40232:22: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 40232 | __pyx_v_idx_array2 = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) ( /* dim=0 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_intp_t const *) __pyx_v_other->idx_array.data) + __pyx_t_1)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_init_node64’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:42113:24: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 42113 | __pyx_v_lower_bounds = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_float64_t const *) ( /* dim=2 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_float64_t const *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_tree->node_bounds.data + __pyx_t_1 * __pyx_v_tree->node_bounds.strides[0]) ) + __pyx_t_2 * __pyx_v_tree->node_bounds.strides[1]) )) + __pyx_t_3)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:42125:24: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 42125 | __pyx_v_upper_bounds = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_float64_t const *) ( /* dim=2 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_float64_t const *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_tree->node_bounds.data + __pyx_t_3 * __pyx_v_tree->node_bounds.strides[0]) ) + __pyx_t_2 * __pyx_v_tree->node_bounds.strides[1]) )) + __pyx_t_1)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_init_node32’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:43940:24: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 43940 | __pyx_v_lower_bounds = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_float32_t const *) ( /* dim=2 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_float32_t const *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_tree->node_bounds.data + __pyx_t_1 * __pyx_v_tree->node_bounds.strides[0]) ) + __pyx_t_2 * __pyx_v_tree->node_bounds.strides[1]) )) + __pyx_t_3)) )))); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:43952:24: warning: assignment discards ‘const’ qualifier from pointer target type [-Wdiscarded-qualifiers] 43952 | __pyx_v_upper_bounds = (&(*((__pyx_t_7sklearn_5utils_9_typedefs_float32_t const *) ( /* dim=2 */ ((char *) (((__pyx_t_7sklearn_5utils_9_typedefs_float32_t const *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_tree->node_bounds.data + __pyx_t_3 * __pyx_v_tree->node_bounds.strides[0]) ) + __pyx_t_2 * __pyx_v_tree->node_bounds.strides[1]) )) + __pyx_t_1)) )))); | ^ In function ‘__pyx_fuse_0__pyx_f_7sklearn_9neighbors_8_kd_tree__total_node_weight’, inlined from ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree32__kde_single_depthfirst’ at sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:39531:19: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:41848:52: warning: ‘__pyx_v_sample_weight’ may be used uninitialized [-Wmaybe-uninitialized] 41848 | __pyx_v_N = (__pyx_v_N + (__pyx_v_sample_weight[(__pyx_v_idx_array[__pyx_v_i])])); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree32__kde_single_depthfirst’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:39109:55: note: ‘__pyx_v_sample_weight’ was declared here 39109 | __pyx_t_7sklearn_5utils_9_typedefs_float32_t const *__pyx_v_sample_weight; | ^~~~~~~~~~~~~~~~~~~~~ In function ‘__pyx_fuse_1__pyx_f_7sklearn_9neighbors_8_kd_tree__total_node_weight’, inlined from ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree64__kde_single_depthfirst’ at sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:25647:19: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:41907:52: warning: ‘__pyx_v_sample_weight’ may be used uninitialized [-Wmaybe-uninitialized] 41907 | __pyx_v_N = (__pyx_v_N + (__pyx_v_sample_weight[(__pyx_v_idx_array[__pyx_v_i])])); | ^ sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c: In function ‘__pyx_f_7sklearn_9neighbors_8_kd_tree_12BinaryTree64__kde_single_depthfirst’: sklearn/neighbors/_kd_tree.cpython-314-powerpc64le-linux-gnu.so.p/_kd_tree.c:25225:55: note: ‘__pyx_v_sample_weight’ was declared here 25225 | __pyx_t_7sklearn_5utils_9_typedefs_float64_t const *__pyx_v_sample_weight; | ^~~~~~~~~~~~~~~~~~~~~ [173/253] Linking static target sklearn/svm/liblibsvm-skl.a [174/253] Compiling C++ object sklearn/svm/_newrand.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__newrand.cpp.o [175/253] Compiling C object sklearn/svm/_libsvm_sparse.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__libsvm_sparse.c.o [176/253] Linking static target sklearn/svm/libliblinear-skl.a [177/253] Compiling C object sklearn/svm/_libsvm.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__libsvm.c.o [178/253] Compiling C object sklearn/svm/_liblinear.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__liblinear.c.o [179/253] Compiling C object sklearn/tree/_splitter.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__splitter.c.o [180/253] Compiling C++ object sklearn/preprocessing/_target_encoder_fast.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__target_encoder_fast.cpp.o [181/253] Compiling C object sklearn/tree/_partitioner.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__partitioner.c.o [182/253] Compiling C object sklearn/tree/_utils.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__utils.c.o [183/253] Compiling C object sklearn/tree/_criterion.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__criterion.c.o [184/253] Compiling C++ object sklearn/tree/_tree.cpython-314-powerpc64le-linux-gnu.so.p/meson-generated__tree.cpp.o In file included from /usr/include/c++/15/ppc64le-redhat-linux/bits/c++allocator.h:33, from /usr/include/c++/15/bits/allocator.h:46, from /usr/include/c++/15/string:45, from /usr/include/c++/15/bits/locale_classes.h:42, from /usr/include/c++/15/bits/ios_base.h:43, from /usr/include/c++/15/ios:46, from sklearn/tree/_tree.cpython-314-powerpc64le-linux-gnu.so.p/_tree.cpp:1186: In member function ‘std::__new_allocator::construct(long*, long const&)void’, inlined from ‘std::allocator_traits >::construct(std::allocator&, long*, long const&)void’ at /usr/include/c++/15/bits/alloc_traits.h:674:17, inlined from ‘std::deque >::_M_push_back_aux(long const&)void’ at /usr/include/c++/15/bits/deque.tcc:501:30, inlined from ‘std::deque >::push_back(long const&)’ at /usr/include/c++/15/bits/stl_deque.h:1615:20, inlined from ‘std::stack > >::push(long const&)’ at /usr/include/c++/15/bits/stl_stack.h:287:20, inlined from ‘__pyx_f_7sklearn_4tree_5_tree__cost_complexity_prune(__Pyx_memviewslice, __pyx_obj_7sklearn_4tree_5_tree_Tree*, __pyx_obj_7sklearn_4tree_5_tree__CCPPruneController*) [clone .isra.0]’ at sklearn/tree/_tree.cpython-314-powerpc64le-linux-gnu.so.p/_tree.cpp:27394:44: /usr/include/c++/15/bits/new_allocator.h:191:11: warning: ‘__pyx_v_pruned_branch_node_idx’ may be used uninitialized [-Wmaybe-uninitialized] 191 | { ::new((void *)__p) _Up(std::forward<_Args>(__args)...); } | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ sklearn/tree/_tree.cpython-314-powerpc64le-linux-gnu.so.p/_tree.cpp: In function ‘__pyx_f_7sklearn_4tree_5_tree__cost_complexity_prune(__Pyx_memviewslice, __pyx_obj_7sklearn_4tree_5_tree_Tree*, __pyx_obj_7sklearn_4tree_5_tree__CCPPruneController*) [clone .isra.0]’: sklearn/tree/_tree.cpython-314-powerpc64le-linux-gnu.so.p/_tree.cpp:26430:45: note: ‘__pyx_v_pruned_branch_node_idx’ was declared here 26430 | __pyx_t_7sklearn_5utils_9_typedefs_intp_t __pyx_v_pruned_branch_node_idx; | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ [185/253] Linking target sklearn/__check_build/_check_build.cpython-314-powerpc64le-linux-gnu.so [186/253] Linking target sklearn/_cyutility.cpython-314-powerpc64le-linux-gnu.so [187/253] Linking target sklearn/_isotonic.cpython-314-powerpc64le-linux-gnu.so [188/253] Linking target sklearn/utils/_cython_blas.cpython-314-powerpc64le-linux-gnu.so [189/253] Linking target sklearn/utils/arrayfuncs.cpython-314-powerpc64le-linux-gnu.so [190/253] Linking target sklearn/utils/murmurhash.cpython-314-powerpc64le-linux-gnu.so [191/253] Linking target sklearn/utils/sparsefuncs_fast.cpython-314-powerpc64le-linux-gnu.so [192/253] Linking target sklearn/utils/_openmp_helpers.cpython-314-powerpc64le-linux-gnu.so [193/253] Linking target 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/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/scikit_learn-1.8.0rc1/sklearn/utils/tests/test_unique.py [951/953] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/scikit_learn-1.8.0rc1/sklearn/utils/tests/test_user_interface.py [952/953] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/scikit_learn-1.8.0rc1/sklearn/utils/tests/test_validation.py [953/953] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/scikit_learn-1.8.0rc1/sklearn/utils/tests/test_weight_vector.py Preparing metadata (pyproject.toml): finished with status 'done' Building wheels for collected packages: scikit-learn Building wheel for scikit-learn (pyproject.toml): started Running command Building wheel for scikit-learn (pyproject.toml) Building wheel for scikit-learn (pyproject.toml): finished with status 'done' Created wheel for scikit-learn: filename=scikit_learn-1.8.0rc1-cp314-cp314-linux_ppc64le.whl size=28505566 sha256=aa38772ecfb85ae6b91a2a20e97c82ce6080cdbbd2175a91b738a266d3b00d6f Stored in directory: /builddir/.cache/pip/wheels/21/9a/ee/57ebc0e5784352f0e16174a7adf9c46725795d46a82cb7ad5c Successfully built scikit-learn + RPM_EC=0 ++ jobs -p + exit 0 Executing(%install): /bin/sh -e /var/tmp/rpm-tmp.q23mH5 + umask 022 + cd /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build + '[' /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT '!=' / ']' + rm -rf /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT ++ dirname /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT + mkdir -p /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build + mkdir /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection ' + export CFLAGS + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection ' + export CXXFLAGS + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection -I/usr/lib64/gfortran/modules ' + export FFLAGS + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection -I/usr/lib64/gfortran/modules ' + export FCFLAGS + VALAFLAGS=-g + export VALAFLAGS + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn' + export RUSTFLAGS + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes ' + export LDFLAGS + LT_SYS_LIBRARY_PATH=/usr/lib64: + export LT_SYS_LIBRARY_PATH + CC=gcc + export CC + CXX=g++ + export CXX + cd scikit_learn-1.8.0rc1 ++ ls /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/pyproject-wheeldir/scikit_learn-1.8.0rc1-cp314-cp314-linux_ppc64le.whl ++ xargs basename --multiple ++ sed -E 's/([^-]+)-([^-]+)-.+\.whl/\1==\2/' + specifier=scikit_learn==1.8.0rc1 + '[' -z scikit_learn==1.8.0rc1 ']' + TMPDIR=/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/.pyproject-builddir + /usr/bin/python3 -m pip install --root /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT --prefix /usr --no-deps --disable-pip-version-check --progress-bar off --verbose --ignore-installed --no-warn-script-location --no-index --no-cache-dir --find-links /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/pyproject-wheeldir scikit_learn==1.8.0rc1 Using pip 25.3 from /usr/lib/python3.14/site-packages/pip (python 3.14) Looking in links: /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/pyproject-wheeldir Processing /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/pyproject-wheeldir/scikit_learn-1.8.0rc1-cp314-cp314-linux_ppc64le.whl Installing collected packages: scikit_learn Successfully installed scikit_learn-1.8.0rc1 + '[' -d /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/bin ']' + rm -f /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/python-scikit-learn-1.8.0~rc1-1.fc44.ppc64le-pyproject-ghost-distinfo + site_dirs=() + '[' -d /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib/python3.14/site-packages ']' + '[' /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages '!=' /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib/python3.14/site-packages ']' + '[' -d /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages ']' + site_dirs+=("/usr/lib64/python3.14/site-packages") + for site_dir in ${site_dirs[@]} + for distinfo in /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT$site_dir/*.dist-info + echo '%ghost %dir /usr/lib64/python3.14/site-packages/scikit_learn-1.8.0rc1.dist-info' + sed -i s/pip/rpm/ /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/scikit_learn-1.8.0rc1.dist-info/INSTALLER + PYTHONPATH=/usr/lib/rpm/redhat + /usr/bin/python3 -B /usr/lib/rpm/redhat/pyproject_preprocess_record.py --buildroot /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT --record /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/scikit_learn-1.8.0rc1.dist-info/RECORD --output /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/python-scikit-learn-1.8.0~rc1-1.fc44.ppc64le-pyproject-record + rm -fv /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/scikit_learn-1.8.0rc1.dist-info/RECORD removed '/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/scikit_learn-1.8.0rc1.dist-info/RECORD' removed '/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/scikit_learn-1.8.0rc1.dist-info/REQUESTED' + rm -fv /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/scikit_learn-1.8.0rc1.dist-info/REQUESTED ++ wc -l /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/python-scikit-learn-1.8.0~rc1-1.fc44.ppc64le-pyproject-ghost-distinfo ++ cut -f1 '-d ' + lines=1 + '[' 1 -ne 1 ']' + RPM_FILES_ESCAPE=4.19 + /usr/bin/python3 /usr/lib/rpm/redhat/pyproject_save_files.py --output-files /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/python-scikit-learn-1.8.0~rc1-1.fc44.ppc64le-pyproject-files --output-modules /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/python-scikit-learn-1.8.0~rc1-1.fc44.ppc64le-pyproject-modules --buildroot /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT --sitelib /usr/lib/python3.14/site-packages --sitearch /usr/lib64/python3.14/site-packages --python-version 3.14 --pyproject-record /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/python-scikit-learn-1.8.0~rc1-1.fc44.ppc64le-pyproject-record --prefix /usr sklearn + /usr/bin/find-debuginfo -j2 --strict-build-id -m -i --build-id-seed 1.8.0~rc1-1.fc44 --unique-debug-suffix -1.8.0~rc1-1.fc44.ppc64le --unique-debug-src-base python-scikit-learn-1.8.0~rc1-1.fc44.ppc64le --run-dwz --dwz-low-mem-die-limit 10000000 --dwz-max-die-limit 50000000 -S debugsourcefiles.list /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/scikit_learn-1.8.0rc1 find-debuginfo: starting Extracting debug info from 69 files DWARF-compressing 69 files sepdebugcrcfix: Updated 69 CRC32s, 0 CRC32s did match. 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directory find-debuginfo: done + /usr/lib/rpm/check-buildroot + /usr/lib/rpm/redhat/brp-ldconfig + COMPRESS='gzip -9 -n' + COMPRESS_EXT=.gz + /usr/lib/rpm/brp-compress + /usr/lib/rpm/redhat/brp-strip-lto /usr/bin/strip + /usr/lib/rpm/check-rpaths + /usr/lib/rpm/redhat/brp-mangle-shebangs mangling shebang in /usr/lib64/python3.14/site-packages/sklearn/_build_utils/version.py from /usr/bin/env python3 to #!/usr/bin/python3 + /usr/lib/rpm/brp-remove-la-files + /usr/lib/rpm/redhat/brp-python-rpm-in-distinfo + env /usr/lib/rpm/redhat/brp-python-bytecompile '' 1 0 -j2 Bytecompiling .py files below /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib/debug/usr/lib64/python3.14 using python3.14 Bytecompiling .py files below /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14 using python3.14 + /usr/lib/rpm/redhat/brp-python-hardlink + /usr/bin/add-det --brp -j2 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT 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Scanned 270 directories and 2290 files, processed 965 inodes, 883 modified (518 replaced + 365 rewritten), 0 unsupported format, 0 errors + /usr/bin/linkdupes --brp /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr Scanned 269 directories and 2290 files, considered 2290 files, read 218 files, linked 66 files, 0 errors sum of sizes of linked files: 938 bytes Reading /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/SPECPARTS/rpm-debuginfo.specpart Executing(%check): /bin/sh -e /var/tmp/rpm-tmp.1ISxI3 + umask 022 + cd /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build + CFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection ' + export CFLAGS + CXXFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Werror=format-security -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection ' ~/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages ~/build/BUILD/python-scikit-learn-1.8.0_rc1-build/scikit_learn-1.8.0rc1 + export CXXFLAGS + FFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection -I/usr/lib64/gfortran/modules ' + export FFLAGS + FCFLAGS='-O2 -flto=auto -ffat-lto-objects -fexceptions -g -grecord-gcc-switches -pipe -Wall -Wp,-U_FORTIFY_SOURCE,-D_FORTIFY_SOURCE=3 -Wp,-D_GLIBCXX_ASSERTIONS -specs=/usr/lib/rpm/redhat/redhat-hardened-cc1 -fstack-protector-strong -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -m64 -mcpu=power8 -mtune=power8 -fasynchronous-unwind-tables -fstack-clash-protection -I/usr/lib64/gfortran/modules ' + export FCFLAGS + VALAFLAGS=-g + export VALAFLAGS + RUSTFLAGS='-Copt-level=3 -Cdebuginfo=2 -Ccodegen-units=1 -Cstrip=none -Clink-arg=-specs=/usr/lib/rpm/redhat/redhat-package-notes --cap-lints=warn' + export RUSTFLAGS + LDFLAGS='-Wl,-z,relro -Wl,--as-needed -Wl,-z,pack-relative-relocs -Wl,-z,now -specs=/usr/lib/rpm/redhat/redhat-hardened-ld -specs=/usr/lib/rpm/redhat/redhat-hardened-ld-errors -specs=/usr/lib/rpm/redhat/redhat-annobin-cc1 -Wl,--build-id=sha1 -specs=/usr/lib/rpm/redhat/redhat-package-notes ' + export LDFLAGS + LT_SYS_LIBRARY_PATH=/usr/lib64: + export LT_SYS_LIBRARY_PATH + CC=gcc + export CC + CXX=g++ + export CXX + cd scikit_learn-1.8.0rc1 + export PYTHONDONTWRITEBYTECODE=1 + PYTHONDONTWRITEBYTECODE=1 + export 'PYTEST_ADDOPTS=-p no:cacheprovider' + PYTEST_ADDOPTS='-p no:cacheprovider' + pushd /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages + pytest sklearn ============================= test session starts ============================== platform linux -- Python 3.14.2, pytest-8.4.2, pluggy-1.6.0 rootdir: /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages collected 41765 items / 1 skipped sklearn/_loss/tests/test_link.py ....................... 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[100%] =================================== FAILURES =================================== _____________________________ test_cluster_qr[42] ______________________________ global_random_seed = 42 def test_cluster_qr(global_random_seed): # cluster_qr by itself should not be used for clustering generic data # other than the rows of the eigenvectors within spectral clustering, # but cluster_qr must still preserve the labels for different dtypes # of the generic fixed input even if the labels may be meaningless. random_state = np.random.RandomState(seed=global_random_seed) n_samples, n_components = 10, 5 data = random_state.randn(n_samples, n_components) labels_float64 = cluster_qr(data.astype(np.float64)) # Each sample is assigned a cluster identifier assert labels_float64.shape == (n_samples,) # All components should be covered by the assignment assert np.array_equal(np.unique(labels_float64), np.arange(n_components)) # Single precision data should yield the same cluster assignments labels_float32 = cluster_qr(data.astype(np.float32)) > assert np.array_equal(labels_float64, labels_float32) E assert False E + where False = (array([0, 2, 1, 1, 3, 1, 2, 0, 4, 1]), array([2, 3, 2, 2, 0, 4, 4, 4, 1, 4])) E + where = np.array_equal sklearn/cluster/tests/test_spectral.py:190: AssertionError ________________ test_dict_learning_numerical_consistency[lars] ________________ method = 'lars' @pytest.mark.parametrize("method", ("lars", "cd")) def test_dict_learning_numerical_consistency(method): # verify numerically consistent among np.float32 and np.float64 rtol = 1e-4 n_components = 4 alpha = 2 U_64, V_64, _ = dict_learning( X.astype(np.float64), n_components=n_components, alpha=alpha, random_state=0, method=method, ) U_32, V_32, _ = dict_learning( X.astype(np.float32), n_components=n_components, alpha=alpha, random_state=0, method=method, ) # Optimal solution (U*, V*) is not unique. # If (U*, V*) is optimal solution, (-U*,-V*) is also optimal, # and (column permutated U*, row permutated V*) are also optional # as long as holding UV. # So here UV, ||U||_1,1 and sum(||V_k||_2^2) are verified # instead of comparing directly U and V. > assert_allclose(np.matmul(U_64, V_64), np.matmul(U_32, V_32), rtol=rtol) sklearn/decomposition/tests/test_dict_learning.py:892: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([[ 0.84133563, 0.19084837, 0.46679291, 1.06875699, 0.89070093, -0.46609654, 0.45312898, -0.07218732... [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]]) desired = array([[ 0.8413358 , 0.1908484 , 0.46679297, 1.068757 , 0.89070106, -0.4660966 , 0.45312902, -0.07218733... , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]], dtype=float32) rtol = 0.0001, atol = 0.0, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.0001, atol=0 E E Mismatched elements: 16 / 80 (20%) E Max absolute difference among violations: 1.03706031 E Max relative difference among violations: 1. E ACTUAL: array([[ 0.841336, 0.190848, 0.466793, 1.068757, 0.890701, -0.466097, E 0.453129, -0.072187], E [ 0. , 0. , 0. , 0. , 0. , 0. ,... E DESIRED: array([[ 0.841336, 0.190848, 0.466793, 1.068757, 0.890701, -0.466097, E 0.453129, -0.072187], E [ 0. , 0. , 0. , 0. , 0. , 0. ,... sklearn/utils/_testing.py:233: AssertionError _________________ test_dict_learning_numerical_consistency[cd] _________________ method = 'cd' @pytest.mark.parametrize("method", ("lars", "cd")) def test_dict_learning_numerical_consistency(method): # verify numerically consistent among np.float32 and np.float64 rtol = 1e-4 n_components = 4 alpha = 2 U_64, V_64, _ = dict_learning( X.astype(np.float64), n_components=n_components, alpha=alpha, random_state=0, method=method, ) U_32, V_32, _ = dict_learning( X.astype(np.float32), n_components=n_components, alpha=alpha, random_state=0, method=method, ) # Optimal solution (U*, V*) is not unique. # If (U*, V*) is optimal solution, (-U*,-V*) is also optimal, # and (column permutated U*, row permutated V*) are also optional # as long as holding UV. # So here UV, ||U||_1,1 and sum(||V_k||_2^2) are verified # instead of comparing directly U and V. > assert_allclose(np.matmul(U_64, V_64), np.matmul(U_32, V_32), rtol=rtol) sklearn/decomposition/tests/test_dict_learning.py:892: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([[ 0.84133563, 0.19084837, 0.46679291, 1.06875699, 0.89070093, -0.46609654, 0.45312898, -0.07218732... [ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]]) desired = array([[ 0.8413356 , 0.19084837, 0.4667929 , 1.0687568 , 0.890701 , -0.4660965 , 0.45312896, -0.07218731... , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]], dtype=float32) rtol = 0.0001, atol = 0.0, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.0001, atol=0 E E Mismatched elements: 16 / 80 (20%) E Max absolute difference among violations: 1.03706031 E Max relative difference among violations: 1. E ACTUAL: array([[ 0.841336, 0.190848, 0.466793, 1.068757, 0.890701, -0.466097, E 0.453129, -0.072187], E [ 0. , 0. , 0. , 0. , 0. , 0. ,... E DESIRED: array([[ 0.841336, 0.190848, 0.466793, 1.068757, 0.890701, -0.466096, E 0.453129, -0.072187], E [ 0. , 0. , 0. , 0. , 0. , 0. ,... sklearn/utils/_testing.py:233: AssertionError ____________ test_dict_learning_online_numerical_consistency[lars] _____________ method = 'lars' @pytest.mark.parametrize("method", ("lars", "cd")) def test_dict_learning_online_numerical_consistency(method): # verify numerically consistent among np.float32 and np.float64 rtol = 1e-4 n_components = 4 alpha = 1 U_64, V_64 = dict_learning_online( X.astype(np.float64), n_components=n_components, max_iter=1_000, alpha=alpha, batch_size=10, random_state=0, method=method, tol=0.0, max_no_improvement=None, ) U_32, V_32 = dict_learning_online( X.astype(np.float32), n_components=n_components, max_iter=1_000, alpha=alpha, batch_size=10, random_state=0, method=method, tol=0.0, max_no_improvement=None, ) # Optimal solution (U*, V*) is not unique. # If (U*, V*) is optimal solution, (-U*,-V*) is also optimal, # and (column permutated U*, row permutated V*) are also optional # as long as holding UV. # So here UV, ||U||_1,1 and sum(||V_k||_2) are verified # instead of comparing directly U and V. > assert_allclose(np.matmul(U_64, V_64), np.matmul(U_32, V_32), rtol=rtol) sklearn/decomposition/tests/test_dict_learning.py:962: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([[ 1.23443639, 0.32008655, 0.70347928, 1.71447757, 1.38721264, -0.67791897, 0.71095516, -0.07592906... [ 0.17976632, -0.0434607 , 0.04051646, -0.10334679, -0.28843614, 0.05988512, 0.0871053 , -0.07846401]]) desired = array([[ 1.2344352 , 0.32008642, 0.70348 , 1.7144802 , 1.3872086 , -0.6779204 , 0.7109575 , -0.07592898...84, -0.043441 , 0.04050796, -0.10332682, -0.28839058, 0.0598797 , 0.08709645, -0.07845277]], dtype=float32) rtol = 0.0001, atol = 0.0, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.0001, atol=0 E E Mismatched elements: 46 / 80 (57.5%) E Max absolute difference among violations: 0.00241467 E Max relative difference among violations: 0.05793222 E ACTUAL: array([[ 1.234436, 0.320087, 0.703479, 1.714478, 1.387213, -0.677919, E 0.710955, -0.075929], E [ 0.161607, 0.041904, 0.092096, 0.224452, 0.181608, -0.08875 ,... E DESIRED: array([[ 1.234435, 0.320086, 0.70348 , 1.71448 , 1.387209, -0.67792 , E 0.710958, -0.075929], E [ 0.161607, 0.041904, 0.092097, 0.224452, 0.181607, -0.08875 ,... sklearn/utils/_testing.py:233: AssertionError _____________ test_dict_learning_online_numerical_consistency[cd] ______________ method = 'cd' @pytest.mark.parametrize("method", ("lars", "cd")) def test_dict_learning_online_numerical_consistency(method): # verify numerically consistent among np.float32 and np.float64 rtol = 1e-4 n_components = 4 alpha = 1 U_64, V_64 = dict_learning_online( X.astype(np.float64), n_components=n_components, max_iter=1_000, alpha=alpha, batch_size=10, random_state=0, method=method, tol=0.0, max_no_improvement=None, ) U_32, V_32 = dict_learning_online( X.astype(np.float32), n_components=n_components, max_iter=1_000, alpha=alpha, batch_size=10, random_state=0, method=method, tol=0.0, max_no_improvement=None, ) # Optimal solution (U*, V*) is not unique. # If (U*, V*) is optimal solution, (-U*,-V*) is also optimal, # and (column permutated U*, row permutated V*) are also optional # as long as holding UV. # So here UV, ||U||_1,1 and sum(||V_k||_2) are verified # instead of comparing directly U and V. > assert_allclose(np.matmul(U_64, V_64), np.matmul(U_32, V_32), rtol=rtol) sklearn/decomposition/tests/test_dict_learning.py:962: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([[ 1.23443639, 0.32008655, 0.70347928, 1.71447757, 1.38721264, -0.67791897, 0.71095516, -0.07592906... [ 0.17976632, -0.0434607 , 0.04051646, -0.10334679, -0.28843614, 0.05988512, 0.0871053 , -0.07846401]]) desired = array([[ 1.2344352 , 0.32008645, 0.70348006, 1.7144803 , 1.3872086 , -0.67792046, 0.7109576 , -0.07592899...84, -0.04344099, 0.04050796, -0.1033268 , -0.28839058, 0.0598797 , 0.08709645, -0.07845277]], dtype=float32) rtol = 0.0001, atol = 0.0, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.0001, atol=0 E E Mismatched elements: 46 / 80 (57.5%) E Max absolute difference among violations: 0.00241467 E Max relative difference among violations: 0.05793853 E ACTUAL: array([[ 1.234436, 0.320087, 0.703479, 1.714478, 1.387213, -0.677919, E 0.710955, -0.075929], E [ 0.161607, 0.041904, 0.092096, 0.224452, 0.181608, -0.08875 ,... E DESIRED: array([[ 1.234435, 0.320086, 0.70348 , 1.71448 , 1.387209, -0.67792 , E 0.710958, -0.075929], E [ 0.161607, 0.041904, 0.092097, 0.224452, 0.181607, -0.08875 ,... sklearn/utils/_testing.py:233: AssertionError ________________________ test_32_64_decomposition_shape ________________________ def test_32_64_decomposition_shape(): """Test that the decomposition is similar for 32 and 64 bits data Non regression test for https://github.com/scikit-learn/scikit-learn/issues/18146 """ X, y = make_blobs( n_samples=30, centers=[[0, 0, 0], [1, 1, 1]], random_state=0, cluster_std=0.1 ) X = StandardScaler().fit_transform(X) X -= X.min() # Compare the shapes (corresponds to the number of non-zero eigenvalues) kpca = KernelPCA() > assert kpca.fit_transform(X).shape == kpca.fit_transform(X.astype(np.float32)).shape ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/tests/test_kernel_pca.py:517: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ sklearn/utils/_set_output.py:316: in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/_kernel_pca.py:476: in fit_transform self.fit(X, **params) sklearn/base.py:1336: in wrapper return fit_method(estimator, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/_kernel_pca.py:444: in fit self._fit_transform_in_place(K) sklearn/decomposition/_kernel_pca.py:368: in _fit_transform_in_place self.eigenvalues_ = _check_psd_eigenvalues( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ lambdas = array([-1.9319580e+01, -3.1356487e+00, -2.5093613e+00, -1.7023106e+00, -8.8473892e-01, -5.7023621e-01, -4.23841... 9.6709365e-01, 1.0699463e+00, 2.4626904e+00, 2.6019115e+00, 1.8645788e+01, 9.0205933e+01], dtype=float32) enable_warnings = False def _check_psd_eigenvalues(lambdas, enable_warnings=False): """Check the eigenvalues of a positive semidefinite (PSD) matrix. Checks the provided array of PSD matrix eigenvalues for numerical or conditioning issues and returns a fixed validated version. This method should typically be used if the PSD matrix is user-provided (e.g. a Gram matrix) or computed using a user-provided dissimilarity metric (e.g. kernel function), or if the decomposition process uses approximation methods (randomized SVD, etc.). It checks for three things: - that there are no significant imaginary parts in eigenvalues (more than 1e-5 times the maximum real part). If this check fails, it raises a ``ValueError``. Otherwise all non-significant imaginary parts that may remain are set to zero. This operation is traced with a ``PositiveSpectrumWarning`` when ``enable_warnings=True``. - that eigenvalues are not all negative. If this check fails, it raises a ``ValueError`` - that there are no significant negative eigenvalues with absolute value more than 1e-10 (1e-6) and more than 1e-5 (5e-3) times the largest positive eigenvalue in double (simple) precision. If this check fails, it raises a ``ValueError``. Otherwise all negative eigenvalues that may remain are set to zero. This operation is traced with a ``PositiveSpectrumWarning`` when ``enable_warnings=True``. Finally, all the positive eigenvalues that are too small (with a value smaller than the maximum eigenvalue multiplied by 1e-12 (2e-7)) are set to zero. This operation is traced with a ``PositiveSpectrumWarning`` when ``enable_warnings=True``. Parameters ---------- lambdas : array-like of shape (n_eigenvalues,) Array of eigenvalues to check / fix. enable_warnings : bool, default=False When this is set to ``True``, a ``PositiveSpectrumWarning`` will be raised when there are imaginary parts, negative eigenvalues, or extremely small non-zero eigenvalues. Otherwise no warning will be raised. In both cases, imaginary parts, negative eigenvalues, and extremely small non-zero eigenvalues will be set to zero. Returns ------- lambdas_fixed : ndarray of shape (n_eigenvalues,) A fixed validated copy of the array of eigenvalues. Examples -------- >>> from sklearn.utils.validation import _check_psd_eigenvalues >>> _check_psd_eigenvalues([1, 2]) # nominal case array([1, 2]) >>> _check_psd_eigenvalues([5, 5j]) # significant imag part Traceback (most recent call last): ... ValueError: There are significant imaginary parts in eigenvalues (1 of the maximum real part). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. >>> _check_psd_eigenvalues([5, 5e-5j]) # insignificant imag part array([5., 0.]) >>> _check_psd_eigenvalues([-5, -1]) # all negative Traceback (most recent call last): ... ValueError: All eigenvalues are negative (maximum is -1). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. >>> _check_psd_eigenvalues([5, -1]) # significant negative Traceback (most recent call last): ... ValueError: There are significant negative eigenvalues (0.2 of the maximum positive). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. >>> _check_psd_eigenvalues([5, -5e-5]) # insignificant negative array([5., 0.]) >>> _check_psd_eigenvalues([5, 4e-12]) # bad conditioning (too small) array([5., 0.]) """ lambdas = np.array(lambdas) is_double_precision = lambdas.dtype == np.float64 # note: the minimum value available is # - single-precision: np.finfo('float32').eps = 1.2e-07 # - double-precision: np.finfo('float64').eps = 2.2e-16 # the various thresholds used for validation # we may wish to change the value according to precision. significant_imag_ratio = 1e-5 significant_neg_ratio = 1e-5 if is_double_precision else 5e-3 significant_neg_value = 1e-10 if is_double_precision else 1e-6 small_pos_ratio = 1e-12 if is_double_precision else 2e-7 # Check that there are no significant imaginary parts if not np.isreal(lambdas).all(): max_imag_abs = np.abs(np.imag(lambdas)).max() max_real_abs = np.abs(np.real(lambdas)).max() if max_imag_abs > significant_imag_ratio * max_real_abs: raise ValueError( "There are significant imaginary parts in eigenvalues (%g " "of the maximum real part). Either the matrix is not PSD, or " "there was an issue while computing the eigendecomposition " "of the matrix." % (max_imag_abs / max_real_abs) ) # warn about imaginary parts being removed if enable_warnings: warnings.warn( "There are imaginary parts in eigenvalues (%g " "of the maximum real part). Either the matrix is not" " PSD, or there was an issue while computing the " "eigendecomposition of the matrix. Only the real " "parts will be kept." % (max_imag_abs / max_real_abs), PositiveSpectrumWarning, ) # Remove all imaginary parts (even if zero) lambdas = np.real(lambdas) # Check that there are no significant negative eigenvalues max_eig = lambdas.max() if max_eig < 0: raise ValueError( "All eigenvalues are negative (maximum is %g). " "Either the matrix is not PSD, or there was an " "issue while computing the eigendecomposition of " "the matrix." % max_eig ) else: min_eig = lambdas.min() if ( min_eig < -significant_neg_ratio * max_eig and min_eig < -significant_neg_value ): > raise ValueError( "There are significant negative eigenvalues (%g" " of the maximum positive). Either the matrix is " "not PSD, or there was an issue while computing " "the eigendecomposition of the matrix." % (-min_eig / max_eig) E ValueError: There are significant negative eigenvalues (0.214172 of the maximum positive). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. sklearn/utils/validation.py:2044: ValueError ______________ test_nmf_dtype_match[NMF-solver0-float32-float32] _______________ Estimator = , solver = {'solver': 'cd'} dtype_in = , dtype_out = @pytest.mark.parametrize( "dtype_in, dtype_out", [ (np.float32, np.float32), (np.float64, np.float64), (np.int32, np.float64), (np.int64, np.float64), ], ) @pytest.mark.parametrize( ["Estimator", "solver"], [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]], ) def test_nmf_dtype_match(Estimator, solver, dtype_in, dtype_out): # Check that NMF preserves dtype (float32 and float64) X = np.random.RandomState(0).randn(20, 15).astype(dtype_in, copy=False) np.abs(X, out=X) nmf = Estimator( alpha_W=1.0, alpha_H=1.0, tol=1e-2, random_state=0, **solver, ) > assert nmf.fit(X).transform(X).dtype == dtype_out ^^^^^^^^^^ sklearn/decomposition/tests/test_nmf.py:799: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ sklearn/decomposition/_nmf.py:1282: in fit self.fit_transform(X, **params) sklearn/utils/_set_output.py:316: in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/base.py:1336: in wrapper return fit_method(estimator, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/_nmf.py:1618: in fit_transform W, H, n_iter = self._fit_transform(X, W=W, H=H) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/_nmf.py:1680: in _fit_transform W, H = self._check_w_h(X, W, H, update_H) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/_nmf.py:1240: in _check_w_h W, H = _initialize_nmf( sklearn/decomposition/_nmf.py:309: in _initialize_nmf U, S, V = _randomized_svd(X, n_components, random_state=random_state) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/utils/extmath.py:611: in _randomized_svd Uhat, s, Vt = linalg.svd( /usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: in wrapper return f(*arrays, *other_args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib64/python3.14/site-packages/scipy/linalg/_decomp_svd.py:110: in svd a1 = _asarray_validated(a, check_finite=check_finite) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:455: in _asarray_validated a = toarray(a) ^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ a = array([[-2.7556787e+00, -4.2194781e+00, -3.3899853e+00, -5.1511607e+00, -3.0845611e+00, -4.5174432e+00, -3.041... nan, nan, nan, nan, nan, nan]], dtype=float32) dtype = None, order = None @set_module('numpy') def asarray_chkfinite(a, dtype=None, order=None): """Convert the input to an array, checking for NaNs or Infs. Parameters ---------- a : array_like Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays. Success requires no NaNs or Infs. dtype : data-type, optional By default, the data-type is inferred from the input data. order : {'C', 'F', 'A', 'K'}, optional Memory layout. 'A' and 'K' depend on the order of input array a. 'C' row-major (C-style), 'F' column-major (Fortran-style) memory representation. 'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise 'K' (keep) preserve input order Defaults to 'C'. Returns ------- out : ndarray Array interpretation of `a`. No copy is performed if the input is already an ndarray. If `a` is a subclass of ndarray, a base class ndarray is returned. Raises ------ ValueError Raises ValueError if `a` contains NaN (Not a Number) or Inf (Infinity). See Also -------- asarray : Create and array. asanyarray : Similar function which passes through subclasses. ascontiguousarray : Convert input to a contiguous array. asfortranarray : Convert input to an ndarray with column-major memory order. fromiter : Create an array from an iterator. fromfunction : Construct an array by executing a function on grid positions. Examples -------- >>> import numpy as np Convert a list into an array. If all elements are finite, then ``asarray_chkfinite`` is identical to ``asarray``. >>> a = [1, 2] >>> np.asarray_chkfinite(a, dtype=float) array([1., 2.]) Raises ValueError if array_like contains Nans or Infs. >>> a = [1, 2, np.inf] >>> try: ... np.asarray_chkfinite(a) ... except ValueError: ... print('ValueError') ... ValueError """ a = asarray(a, dtype=dtype, order=order) if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all(): > raise ValueError( "array must not contain infs or NaNs") E ValueError: array must not contain infs or NaNs /usr/lib64/python3.14/site-packages/numpy/lib/_function_base_impl.py:665: ValueError ______________ test_nmf_dtype_match[NMF-solver1-float32-float32] _______________ Estimator = , solver = {'solver': 'mu'} dtype_in = , dtype_out = @pytest.mark.parametrize( "dtype_in, dtype_out", [ (np.float32, np.float32), (np.float64, np.float64), (np.int32, np.float64), (np.int64, np.float64), ], ) @pytest.mark.parametrize( ["Estimator", "solver"], [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]], ) def test_nmf_dtype_match(Estimator, solver, dtype_in, dtype_out): # Check that NMF preserves dtype (float32 and float64) X = np.random.RandomState(0).randn(20, 15).astype(dtype_in, copy=False) np.abs(X, out=X) nmf = Estimator( alpha_W=1.0, alpha_H=1.0, tol=1e-2, random_state=0, **solver, ) > assert nmf.fit(X).transform(X).dtype == dtype_out ^^^^^^^^^^ sklearn/decomposition/tests/test_nmf.py:799: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ sklearn/decomposition/_nmf.py:1282: in fit self.fit_transform(X, **params) sklearn/utils/_set_output.py:316: in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/base.py:1336: in wrapper return fit_method(estimator, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/_nmf.py:1618: in fit_transform W, H, n_iter = self._fit_transform(X, W=W, H=H) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/_nmf.py:1680: in _fit_transform W, H = self._check_w_h(X, W, H, update_H) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/_nmf.py:1240: in _check_w_h W, H = _initialize_nmf( sklearn/decomposition/_nmf.py:309: in _initialize_nmf U, S, V = _randomized_svd(X, n_components, random_state=random_state) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/utils/extmath.py:611: in _randomized_svd Uhat, s, Vt = linalg.svd( /usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: in wrapper return f(*arrays, *other_args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib64/python3.14/site-packages/scipy/linalg/_decomp_svd.py:110: in svd a1 = _asarray_validated(a, check_finite=check_finite) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:455: in _asarray_validated a = toarray(a) ^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ a = array([[-2.7556787e+00, -4.2194781e+00, -3.3899853e+00, -5.1511607e+00, -3.0845611e+00, -4.5174432e+00, -3.041... nan, nan, nan, nan, nan, nan]], dtype=float32) dtype = None, order = None @set_module('numpy') def asarray_chkfinite(a, dtype=None, order=None): """Convert the input to an array, checking for NaNs or Infs. Parameters ---------- a : array_like Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays. Success requires no NaNs or Infs. dtype : data-type, optional By default, the data-type is inferred from the input data. order : {'C', 'F', 'A', 'K'}, optional Memory layout. 'A' and 'K' depend on the order of input array a. 'C' row-major (C-style), 'F' column-major (Fortran-style) memory representation. 'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise 'K' (keep) preserve input order Defaults to 'C'. Returns ------- out : ndarray Array interpretation of `a`. No copy is performed if the input is already an ndarray. If `a` is a subclass of ndarray, a base class ndarray is returned. Raises ------ ValueError Raises ValueError if `a` contains NaN (Not a Number) or Inf (Infinity). See Also -------- asarray : Create and array. asanyarray : Similar function which passes through subclasses. ascontiguousarray : Convert input to a contiguous array. asfortranarray : Convert input to an ndarray with column-major memory order. fromiter : Create an array from an iterator. fromfunction : Construct an array by executing a function on grid positions. Examples -------- >>> import numpy as np Convert a list into an array. If all elements are finite, then ``asarray_chkfinite`` is identical to ``asarray``. >>> a = [1, 2] >>> np.asarray_chkfinite(a, dtype=float) array([1., 2.]) Raises ValueError if array_like contains Nans or Infs. >>> a = [1, 2, np.inf] >>> try: ... np.asarray_chkfinite(a) ... except ValueError: ... print('ValueError') ... ValueError """ a = asarray(a, dtype=dtype, order=order) if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all(): > raise ValueError( "array must not contain infs or NaNs") E ValueError: array must not contain infs or NaNs /usr/lib64/python3.14/site-packages/numpy/lib/_function_base_impl.py:665: ValueError __________ test_nmf_dtype_match[MiniBatchNMF-solver2-float32-float32] __________ Estimator = , solver = {} dtype_in = , dtype_out = @pytest.mark.parametrize( "dtype_in, dtype_out", [ (np.float32, np.float32), (np.float64, np.float64), (np.int32, np.float64), (np.int64, np.float64), ], ) @pytest.mark.parametrize( ["Estimator", "solver"], [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]], ) def test_nmf_dtype_match(Estimator, solver, dtype_in, dtype_out): # Check that NMF preserves dtype (float32 and float64) X = np.random.RandomState(0).randn(20, 15).astype(dtype_in, copy=False) np.abs(X, out=X) nmf = Estimator( alpha_W=1.0, alpha_H=1.0, tol=1e-2, random_state=0, **solver, ) > assert nmf.fit(X).transform(X).dtype == dtype_out ^^^^^^^^^^ sklearn/decomposition/tests/test_nmf.py:799: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ sklearn/decomposition/_nmf.py:1282: in fit self.fit_transform(X, **params) sklearn/utils/_set_output.py:316: in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/base.py:1336: in wrapper return fit_method(estimator, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/_nmf.py:2206: in fit_transform W, H, n_iter, n_steps = self._fit_transform(X, W=W, H=H) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/_nmf.py:2272: in _fit_transform W, H = self._check_w_h(X, W, H, update_H) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/_nmf.py:1240: in _check_w_h W, H = _initialize_nmf( sklearn/decomposition/_nmf.py:309: in _initialize_nmf U, S, V = _randomized_svd(X, n_components, random_state=random_state) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/utils/extmath.py:611: in _randomized_svd Uhat, s, Vt = linalg.svd( /usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: in wrapper return f(*arrays, *other_args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib64/python3.14/site-packages/scipy/linalg/_decomp_svd.py:110: in svd a1 = _asarray_validated(a, check_finite=check_finite) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:455: in _asarray_validated a = toarray(a) ^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ a = array([[-2.7556787e+00, -4.2194781e+00, -3.3899853e+00, -5.1511607e+00, -3.0845611e+00, -4.5174432e+00, -3.041... nan, nan, nan, nan, nan, nan]], dtype=float32) dtype = None, order = None @set_module('numpy') def asarray_chkfinite(a, dtype=None, order=None): """Convert the input to an array, checking for NaNs or Infs. Parameters ---------- a : array_like Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays. Success requires no NaNs or Infs. dtype : data-type, optional By default, the data-type is inferred from the input data. order : {'C', 'F', 'A', 'K'}, optional Memory layout. 'A' and 'K' depend on the order of input array a. 'C' row-major (C-style), 'F' column-major (Fortran-style) memory representation. 'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise 'K' (keep) preserve input order Defaults to 'C'. Returns ------- out : ndarray Array interpretation of `a`. No copy is performed if the input is already an ndarray. If `a` is a subclass of ndarray, a base class ndarray is returned. Raises ------ ValueError Raises ValueError if `a` contains NaN (Not a Number) or Inf (Infinity). See Also -------- asarray : Create and array. asanyarray : Similar function which passes through subclasses. ascontiguousarray : Convert input to a contiguous array. asfortranarray : Convert input to an ndarray with column-major memory order. fromiter : Create an array from an iterator. fromfunction : Construct an array by executing a function on grid positions. Examples -------- >>> import numpy as np Convert a list into an array. If all elements are finite, then ``asarray_chkfinite`` is identical to ``asarray``. >>> a = [1, 2] >>> np.asarray_chkfinite(a, dtype=float) array([1., 2.]) Raises ValueError if array_like contains Nans or Infs. >>> a = [1, 2, np.inf] >>> try: ... np.asarray_chkfinite(a) ... except ValueError: ... print('ValueError') ... ValueError """ a = asarray(a, dtype=dtype, order=order) if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all(): > raise ValueError( "array must not contain infs or NaNs") E ValueError: array must not contain infs or NaNs /usr/lib64/python3.14/site-packages/numpy/lib/_function_base_impl.py:665: ValueError ______________ test_nmf_float32_float64_consistency[NMF-solver0] _______________ Estimator = , solver = {'solver': 'cd'} @pytest.mark.parametrize( ["Estimator", "solver"], [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]], ) def test_nmf_float32_float64_consistency(Estimator, solver): # Check that the result of NMF is the same between float32 and float64 X = np.random.RandomState(0).randn(50, 7) np.abs(X, out=X) nmf32 = Estimator(random_state=0, tol=1e-3, **solver) W32 = nmf32.fit_transform(X.astype(np.float32)) nmf64 = Estimator(random_state=0, tol=1e-3, **solver) W64 = nmf64.fit_transform(X) > assert_allclose(W32, W64, atol=1e-5) sklearn/decomposition/tests/test_nmf.py:817: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([[2.07015723e-01, 7.18632713e-03, 1.14095248e-01, 2.44477659e-01, 3.49631101e-01, 8.39198768e-01, 2.9955....33684720e-02, 3.43381166e-02, 1.61227444e-03, 2.45341703e-01, 1.21657543e-01, 1.12575278e-01]], dtype=float32) desired = array([[2.61022658e-01, 7.45616780e-02, 3.20575817e-02, 8.78707005e-02, 2.56462131e-01, 6.91771258e-01, 5.5747...98955566e-01, 3.11855934e-02, 4.13808165e-02, 4.89533674e-02, 7.00393283e-02, 8.46614366e-02, 3.96772518e-02]]) rtol = 0.0001, atol = 1e-05, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.0001, atol=1e-05 E E Mismatched elements: 346 / 350 (98.9%) E Max absolute difference among violations: 1.12907502 E Max relative difference among violations: 536.80594312 E ACTUAL: array([[2.070157e-01, 7.186327e-03, 1.140952e-01, 2.444777e-01, E 3.496311e-01, 8.391988e-01, 2.995524e-01], E [1.105174e-02, 1.459227e-03, 4.446525e-02, 2.779154e-01,... E DESIRED: array([[2.610227e-01, 7.456168e-02, 3.205758e-02, 8.787070e-02, E 2.564621e-01, 6.917713e-01, 5.574762e-01], E [0.000000e+00, 2.921802e-03, 1.006839e-02, 8.178451e-02,... sklearn/utils/_testing.py:233: AssertionError ______________ test_nmf_float32_float64_consistency[NMF-solver1] _______________ Estimator = , solver = {'solver': 'mu'} @pytest.mark.parametrize( ["Estimator", "solver"], [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]], ) def test_nmf_float32_float64_consistency(Estimator, solver): # Check that the result of NMF is the same between float32 and float64 X = np.random.RandomState(0).randn(50, 7) np.abs(X, out=X) nmf32 = Estimator(random_state=0, tol=1e-3, **solver) W32 = nmf32.fit_transform(X.astype(np.float32)) nmf64 = Estimator(random_state=0, tol=1e-3, **solver) W64 = nmf64.fit_transform(X) > assert_allclose(W32, W64, atol=1e-5) sklearn/decomposition/tests/test_nmf.py:817: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([[3.49248439e-01, 2.62813573e-03, 3.31456751e-01, 1.25829846e-01, 1.75885603e-01, 2.77692199e-01, 1.3707....61853908e-02, 4.65717465e-02, 3.10824141e-02, 1.05297670e-01, 4.35006271e-19, 4.43841219e-02]], dtype=float32) desired = array([[2.81029002e-01, 1.51520577e-01, 2.87245970e-02, 1.60577997e-01, 1.98853690e-01, 4.70672288e-01, 3.9968...10567325e-01, 8.78181139e-02, 9.04925634e-02, 4.96600043e-02, 6.11414532e-02, 6.51752969e-02, 1.10452794e-02]]) rtol = 0.0001, atol = 1e-05, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.0001, atol=1e-05 E E Mismatched elements: 345 / 350 (98.6%) E Max absolute difference among violations: 0.59001978 E Max relative difference among violations: 1.73991414e+40 E ACTUAL: array([[3.492484e-01, 2.628136e-03, 3.314568e-01, 1.258298e-01, E 1.758856e-01, 2.776922e-01, 1.370704e-01], E [2.058480e-02, 7.122579e-24, 1.272288e-02, 6.190218e-02,... E DESIRED: array([[2.810290e-01, 1.515206e-01, 2.872460e-02, 1.605780e-01, E 1.988537e-01, 4.706723e-01, 3.996884e-01], E [6.231729e-16, 1.605496e-01, 3.483735e-07, 6.474837e-03,... sklearn/utils/_testing.py:233: AssertionError __________ test_nmf_float32_float64_consistency[MiniBatchNMF-solver2] __________ Estimator = , solver = {} @pytest.mark.parametrize( ["Estimator", "solver"], [[NMF, {"solver": "cd"}], [NMF, {"solver": "mu"}], [MiniBatchNMF, {}]], ) def test_nmf_float32_float64_consistency(Estimator, solver): # Check that the result of NMF is the same between float32 and float64 X = np.random.RandomState(0).randn(50, 7) np.abs(X, out=X) nmf32 = Estimator(random_state=0, tol=1e-3, **solver) W32 = nmf32.fit_transform(X.astype(np.float32)) nmf64 = Estimator(random_state=0, tol=1e-3, **solver) W64 = nmf64.fit_transform(X) > assert_allclose(W32, W64, atol=1e-5) sklearn/decomposition/tests/test_nmf.py:817: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([[3.01880509e-01, 8.85719203e-07, 3.38377625e-01, 1.38405189e-01, 2.27545395e-01, 3.27447891e-01, 1.7268....75144552e-02, 4.66257185e-02, 3.22646946e-02, 1.39281869e-01, 4.13396439e-29, 5.57047799e-02]], dtype=float32) desired = array([[2.67764119e-01, 1.63119165e-01, 1.39583939e-04, 1.53001336e-01, 1.83861291e-01, 5.48528480e-01, 4.6833...13555450e-01, 1.00517749e-01, 1.04543275e-01, 3.45649397e-09, 7.30827421e-02, 6.84822087e-02, 1.04831353e-05]]) rtol = 0.0001, atol = 1e-05, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.0001, atol=1e-05 E E Mismatched elements: 336 / 350 (96%) E Max absolute difference among violations: 0.84274492 E Max relative difference among violations: 2.22893365e+56 E ACTUAL: array([[3.018805e-01, 8.857192e-07, 3.383776e-01, 1.384052e-01, E 2.275454e-01, 3.274479e-01, 1.726884e-01], E [1.015235e-02, 1.531619e-41, 2.527691e-04, 7.271877e-02,... E DESIRED: array([[2.677641e-01, 1.631192e-01, 1.395839e-04, 1.530013e-01, E 1.838613e-01, 5.485285e-01, 4.683362e-01], E [2.716261e-29, 1.822952e-01, 1.221180e-20, 5.421795e-03,... sklearn/utils/_testing.py:233: AssertionError ___________________ test_lda_numerical_consistency[42-batch] ___________________ learning_method = 'batch', global_random_seed = 42 @pytest.mark.parametrize("learning_method", ("batch", "online")) def test_lda_numerical_consistency(learning_method, global_random_seed): """Check numerical consistency between np.float32 and np.float64.""" rng = np.random.RandomState(global_random_seed) X64 = rng.uniform(size=(20, 10)) X32 = X64.astype(np.float32) lda_64 = LatentDirichletAllocation( n_components=5, random_state=global_random_seed, learning_method=learning_method ).fit(X64) lda_32 = LatentDirichletAllocation( n_components=5, random_state=global_random_seed, learning_method=learning_method ).fit(X32) > assert_allclose(lda_32.components_, lda_64.components_) sklearn/decomposition/tests/test_online_lda.py:488: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([[2.0048526e-01, 2.0024243e-01, 2.0007603e-01, 2.0014440e-01, 2.0047210e-01, 2.0011610e-01, 2.0013653e-0... 2.0047210e-01, 2.0011610e-01, 2.0013653e-01, 2.0008819e-01, 2.0047790e-01, 2.0042709e-01]], dtype=float32) desired = array([[ 0.20038147, 0.20038279, 0.20037335, 0.2003753 , 0.20037951, 0.20037835, 0.20037713, 0.20038211...8627, 10.3147699 , 10.75334234, 10.19918892, 9.97540592, 10.01729798, 10.65443599, 8.62110077, 8.7333403 ]]) rtol = 0.0001, atol = 0.0, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.0001, atol=0 E E Mismatched elements: 50 / 50 (100%) E Max absolute difference among violations: 2264082.04961721 E Max relative difference among violations: 11298784.79802605 E ACTUAL: array([[2.004853e-01, 2.002424e-01, 2.000760e-01, 2.001444e-01, E 2.004721e-01, 2.001161e-01, 2.001365e-01, 2.000882e-01, E 2.004779e-01, 2.004271e-01],... E DESIRED: array([[ 0.200381, 0.200383, 0.200373, 0.200375, 0.20038 , 0.200378, E 0.200377, 0.200382, 0.200372, 0.200367], E [ 0.200381, 0.200383, 0.200373, 0.200375, 0.20038 , 0.200378,... sklearn/utils/_testing.py:233: AssertionError __________________ test_lda_numerical_consistency[42-online] ___________________ learning_method = 'online', global_random_seed = 42 @pytest.mark.parametrize("learning_method", ("batch", "online")) def test_lda_numerical_consistency(learning_method, global_random_seed): """Check numerical consistency between np.float32 and np.float64.""" rng = np.random.RandomState(global_random_seed) X64 = rng.uniform(size=(20, 10)) X32 = X64.astype(np.float32) lda_64 = LatentDirichletAllocation( n_components=5, random_state=global_random_seed, learning_method=learning_method ).fit(X64) lda_32 = LatentDirichletAllocation( n_components=5, random_state=global_random_seed, learning_method=learning_method ).fit(X32) > assert_allclose(lda_32.components_, lda_64.components_) sklearn/decomposition/tests/test_online_lda.py:488: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([[0.37135518, 0.38259846, 0.36976284, 0.3833528 , 0.43564495, 0.3890536 , 0.36553997, 0.40934587, 0.3753...1 , 8.525953 , 8.067097 , 7.9752407 , 7.9639764 , 8.522716 , 6.8520036 , 7.0509768 ]], dtype=float32) desired = array([[0.38675122, 0.39096891, 0.41179438, 0.41874073, 0.44707906, 0.41713095, 0.40682088, 0.43116338, 0.3994..., 7.76464943, 8.26255463, 8.57694663, 8.11740544, 8.0198466 , 7.98606476, 8.53388207, 6.88751474, 7.0736456 ]]) rtol = 0.0001, atol = 0.0, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.0001, atol=0 E E Mismatched elements: 43 / 50 (86%) E Max absolute difference among violations: 0.12836355 E Max relative difference among violations: 0.29039883 E ACTUAL: array([[0.371355, 0.382598, 0.369763, 0.383353, 0.435645, 0.389054, E 0.36554 , 0.409346, 0.375342, 0.326942], E [0.349488, 0.447619, 0.472672, 0.415785, 0.399919, 0.397588,... E DESIRED: array([[0.386751, 0.390969, 0.411794, 0.418741, 0.447079, 0.417131, E 0.406821, 0.431163, 0.399403, 0.337822], E [0.349487, 0.439791, 0.467222, 0.410138, 0.399896, 0.400409,... sklearn/utils/_testing.py:233: AssertionError __________ test_solver_consistency[42-20-float32-0.1-sparse_cg-None] ___________ solver = 'sparse_cg', proportion_nonzero = 0.1, n_samples = 20 dtype = 'float32', sparse_container = None, global_random_seed = 42 @pytest.mark.parametrize( "solver, sparse_container", ( (solver, sparse_container) for (solver, sparse_container) in product( ["cholesky", "sag", "sparse_cg", "lsqr", "saga", "ridgecv"], [None] + CSR_CONTAINERS, ) if sparse_container is None or solver in ["sparse_cg", "ridgecv"] ), ) @pytest.mark.parametrize( "n_samples,dtype,proportion_nonzero", [(20, "float32", 0.1), (40, "float32", 1.0), (20, "float64", 0.2)], ) def test_solver_consistency( solver, proportion_nonzero, n_samples, dtype, sparse_container, global_random_seed ): alpha = 1.0 noise = 50.0 if proportion_nonzero > 0.9 else 500.0 X, y = _make_sparse_offset_regression( bias=10, n_features=30, proportion_nonzero=proportion_nonzero, noise=noise, random_state=global_random_seed, n_samples=n_samples, ) # Manually scale the data to avoid pathological cases. We use # minmax_scale to deal with the sparse case without breaking # the sparsity pattern. X = minmax_scale(X) svd_ridge = Ridge(solver="svd", alpha=alpha).fit(X, y) X = X.astype(dtype, copy=False) y = y.astype(dtype, copy=False) if sparse_container is not None: X = sparse_container(X) if solver == "ridgecv": ridge = RidgeCV(alphas=[alpha]) else: if solver.startswith("sag"): # Avoid ConvergenceWarning for sag and saga solvers. tol = 1e-7 max_iter = 100_000 else: tol = 1e-10 max_iter = None ridge = Ridge( alpha=alpha, solver=solver, max_iter=max_iter, tol=tol, random_state=global_random_seed, ) ridge.fit(X, y) > assert_allclose(ridge.coef_, svd_ridge.coef_, atol=1e-3, rtol=1e-3) sklearn/linear_model/tests/test_ridge.py:796: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], dtype=float32) desired = array([ 2.28458564e+02, 1.48792890e+03, -1.02120476e+02, -8.21312556e+01, 5.90313292e+02, 1.28699785e+03, -9...0, -1.31911390e+02, 3.23107751e+01, 2.25348497e+02, 0.00000000e+00, 7.71369348e+02, 0.00000000e+00]) rtol = 0.001, atol = 0.001, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.001, atol=0.001 E E nan location mismatch: E ACTUAL: array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, E nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, E nan, nan, nan, nan], dtype=float32) E DESIRED: array([ 2.284586e+02, 1.487929e+03, -1.021205e+02, -8.213126e+01, E 5.903133e+02, 1.286998e+03, -9.062435e-01, 7.804910e+02, E -1.365296e+02, 3.264057e+02, 1.154424e-13, 1.720151e+03,... sklearn/utils/_testing.py:233: AssertionError _____________ test_solver_consistency[42-20-float32-0.1-lsqr-None] _____________ solver = 'lsqr', proportion_nonzero = 0.1, n_samples = 20, dtype = 'float32' sparse_container = None, global_random_seed = 42 @pytest.mark.parametrize( "solver, sparse_container", ( (solver, sparse_container) for (solver, sparse_container) in product( ["cholesky", "sag", "sparse_cg", "lsqr", "saga", "ridgecv"], [None] + CSR_CONTAINERS, ) if sparse_container is None or solver in ["sparse_cg", "ridgecv"] ), ) @pytest.mark.parametrize( "n_samples,dtype,proportion_nonzero", [(20, "float32", 0.1), (40, "float32", 1.0), (20, "float64", 0.2)], ) def test_solver_consistency( solver, proportion_nonzero, n_samples, dtype, sparse_container, global_random_seed ): alpha = 1.0 noise = 50.0 if proportion_nonzero > 0.9 else 500.0 X, y = _make_sparse_offset_regression( bias=10, n_features=30, proportion_nonzero=proportion_nonzero, noise=noise, random_state=global_random_seed, n_samples=n_samples, ) # Manually scale the data to avoid pathological cases. We use # minmax_scale to deal with the sparse case without breaking # the sparsity pattern. X = minmax_scale(X) svd_ridge = Ridge(solver="svd", alpha=alpha).fit(X, y) X = X.astype(dtype, copy=False) y = y.astype(dtype, copy=False) if sparse_container is not None: X = sparse_container(X) if solver == "ridgecv": ridge = RidgeCV(alphas=[alpha]) else: if solver.startswith("sag"): # Avoid ConvergenceWarning for sag and saga solvers. tol = 1e-7 max_iter = 100_000 else: tol = 1e-10 max_iter = None ridge = Ridge( alpha=alpha, solver=solver, max_iter=max_iter, tol=tol, random_state=global_random_seed, ) ridge.fit(X, y) > assert_allclose(ridge.coef_, svd_ridge.coef_, atol=1e-3, rtol=1e-3) sklearn/linear_model/tests/test_ridge.py:796: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([ 2401.0513 , 750.3162 , -1088.6124 , -2533.4973 , 533.74896 , 3309.0713 , 45.023582, 1278.... , -227.56429 , -2006.4937 , 339.90033 , 0. , 420.3991 , 0. ], dtype=float32) desired = array([ 2.28458564e+02, 1.48792890e+03, -1.02120476e+02, -8.21312556e+01, 5.90313292e+02, 1.28699785e+03, -9...0, -1.31911390e+02, 3.23107751e+01, 2.25348497e+02, 0.00000000e+00, 7.71369348e+02, 0.00000000e+00]) rtol = 0.001, atol = 0.001, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.001, atol=0.001 E E Mismatched elements: 24 / 30 (80%) E Max absolute difference among violations: 2523.2010841 E Max relative difference among violations: 63.09983033 E ACTUAL: array([ 2401.0513 , 750.3162 , -1088.6124 , -2533.4973 , E 533.74896 , 3309.0713 , 45.023582, 1278.4567 , E -1611.9441 , 294.8797 , 0. , 4243.352 ,... E DESIRED: array([ 2.284586e+02, 1.487929e+03, -1.021205e+02, -8.213126e+01, E 5.903133e+02, 1.286998e+03, -9.062435e-01, 7.804910e+02, E -1.365296e+02, 3.264057e+02, 1.154424e-13, 1.720151e+03,... sklearn/utils/_testing.py:233: AssertionError ___________ test_solver_consistency[42-40-float32-1.0-cholesky-None] ___________ solver = 'cholesky', proportion_nonzero = 1.0, n_samples = 40, dtype = 'float32' sparse_container = None, global_random_seed = 42 @pytest.mark.parametrize( "solver, sparse_container", ( (solver, sparse_container) for (solver, sparse_container) in product( ["cholesky", "sag", "sparse_cg", "lsqr", "saga", "ridgecv"], [None] + CSR_CONTAINERS, ) if sparse_container is None or solver in ["sparse_cg", "ridgecv"] ), ) @pytest.mark.parametrize( "n_samples,dtype,proportion_nonzero", [(20, "float32", 0.1), (40, "float32", 1.0), (20, "float64", 0.2)], ) def test_solver_consistency( solver, proportion_nonzero, n_samples, dtype, sparse_container, global_random_seed ): alpha = 1.0 noise = 50.0 if proportion_nonzero > 0.9 else 500.0 X, y = _make_sparse_offset_regression( bias=10, n_features=30, proportion_nonzero=proportion_nonzero, noise=noise, random_state=global_random_seed, n_samples=n_samples, ) # Manually scale the data to avoid pathological cases. We use # minmax_scale to deal with the sparse case without breaking # the sparsity pattern. X = minmax_scale(X) svd_ridge = Ridge(solver="svd", alpha=alpha).fit(X, y) X = X.astype(dtype, copy=False) y = y.astype(dtype, copy=False) if sparse_container is not None: X = sparse_container(X) if solver == "ridgecv": ridge = RidgeCV(alphas=[alpha]) else: if solver.startswith("sag"): # Avoid ConvergenceWarning for sag and saga solvers. tol = 1e-7 max_iter = 100_000 else: tol = 1e-10 max_iter = None ridge = Ridge( alpha=alpha, solver=solver, max_iter=max_iter, tol=tol, random_state=global_random_seed, ) ridge.fit(X, y) > assert_allclose(ridge.coef_, svd_ridge.coef_, atol=1e-3, rtol=1e-3) sklearn/linear_model/tests/test_ridge.py:796: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([ -142.2883 , -143.49054 , -22.864176, 329.3569 , 169.66042 , -1000.0027 , -435.14505 , 207....544 , 931.7788 , 575.23346 , -86.09781 , 509.46814 , 443.4883 , -1884.4235 ], dtype=float32) desired = array([ 47.2473648 , 22.28196053, 188.02558629, 95.27163797, 35.62150556, -48.16112122, 105.14239266, 205.61...99291, 3.29735456, -1.09620493, 70.11423103, -74.65236352, -14.69814929, -12.76152384, 71.48126144]) rtol = 0.001, atol = 0.001, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.001, atol=0.001 E E Mismatched elements: 30 / 30 (100%) E Max absolute difference among violations: 1955.90472336 E Max relative difference among violations: 851.00421233 E ACTUAL: array([ -142.2883 , -143.49054 , -22.864176, 329.3569 , E 169.66042 , -1000.0027 , -435.14505 , 207.25499 , E 216.16826 , -1029.775 , -331.60132 , 5.742819,... E DESIRED: array([ 47.247365, 22.281961, 188.025586, 95.271638, 35.621506, E -48.161121, 105.142393, 205.611635, 36.006209, -99.461958, E 39.617627, 86.874819, 101.547812, 145.163201, -8.282467,... sklearn/utils/_testing.py:233: AssertionError __________ test_solver_consistency[42-40-float32-1.0-sparse_cg-None] ___________ solver = 'sparse_cg', proportion_nonzero = 1.0, n_samples = 40 dtype = 'float32', sparse_container = None, global_random_seed = 42 @pytest.mark.parametrize( "solver, sparse_container", ( (solver, sparse_container) for (solver, sparse_container) in product( ["cholesky", "sag", "sparse_cg", "lsqr", "saga", "ridgecv"], [None] + CSR_CONTAINERS, ) if sparse_container is None or solver in ["sparse_cg", "ridgecv"] ), ) @pytest.mark.parametrize( "n_samples,dtype,proportion_nonzero", [(20, "float32", 0.1), (40, "float32", 1.0), (20, "float64", 0.2)], ) def test_solver_consistency( solver, proportion_nonzero, n_samples, dtype, sparse_container, global_random_seed ): alpha = 1.0 noise = 50.0 if proportion_nonzero > 0.9 else 500.0 X, y = _make_sparse_offset_regression( bias=10, n_features=30, proportion_nonzero=proportion_nonzero, noise=noise, random_state=global_random_seed, n_samples=n_samples, ) # Manually scale the data to avoid pathological cases. We use # minmax_scale to deal with the sparse case without breaking # the sparsity pattern. X = minmax_scale(X) svd_ridge = Ridge(solver="svd", alpha=alpha).fit(X, y) X = X.astype(dtype, copy=False) y = y.astype(dtype, copy=False) if sparse_container is not None: X = sparse_container(X) if solver == "ridgecv": ridge = RidgeCV(alphas=[alpha]) else: if solver.startswith("sag"): # Avoid ConvergenceWarning for sag and saga solvers. tol = 1e-7 max_iter = 100_000 else: tol = 1e-10 max_iter = None ridge = Ridge( alpha=alpha, solver=solver, max_iter=max_iter, tol=tol, random_state=global_random_seed, ) ridge.fit(X, y) > assert_allclose(ridge.coef_, svd_ridge.coef_, atol=1e-3, rtol=1e-3) sklearn/linear_model/tests/test_ridge.py:796: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([ -35326.977, 21460.527, 194970.66 , 106628.53 , -514377.7 , -348473.34 , -46556.754, -67183.62 , ...-391703.66 , 375132.9 , 163971.98 , -303940.5 , -324700.47 , -178008.12 , -162001.81 ], dtype=float32) desired = array([ 47.2473648 , 22.28196053, 188.02558629, 95.27163797, 35.62150556, -48.16112122, 105.14239266, 205.61...99291, 3.29735456, -1.09620493, 70.11423103, -74.65236352, -14.69814929, -12.76152384, 71.48126144]) rtol = 0.001, atol = 0.001, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.001, atol=0.001 E E Mismatched elements: 30 / 30 (100%) E Max absolute difference among violations: 673656.14223471 E Max relative difference among violations: 342211.56280395 E ACTUAL: array([ -35326.977, 21460.527, 194970.66 , 106628.53 , -514377.7 , E -348473.34 , -46556.754, -67183.62 , -96612.47 , 366827.3 , E -76474.016, -32607.697, -370702.94 , -171342.78 , -15597.067,... E DESIRED: array([ 47.247365, 22.281961, 188.025586, 95.271638, 35.621506, E -48.161121, 105.142393, 205.611635, 36.006209, -99.461958, E 39.617627, 86.874819, 101.547812, 145.163201, -8.282467,... sklearn/utils/_testing.py:233: AssertionError _____________ test_solver_consistency[42-40-float32-1.0-lsqr-None] _____________ solver = 'lsqr', proportion_nonzero = 1.0, n_samples = 40, dtype = 'float32' sparse_container = None, global_random_seed = 42 @pytest.mark.parametrize( "solver, sparse_container", ( (solver, sparse_container) for (solver, sparse_container) in product( ["cholesky", "sag", "sparse_cg", "lsqr", "saga", "ridgecv"], [None] + CSR_CONTAINERS, ) if sparse_container is None or solver in ["sparse_cg", "ridgecv"] ), ) @pytest.mark.parametrize( "n_samples,dtype,proportion_nonzero", [(20, "float32", 0.1), (40, "float32", 1.0), (20, "float64", 0.2)], ) def test_solver_consistency( solver, proportion_nonzero, n_samples, dtype, sparse_container, global_random_seed ): alpha = 1.0 noise = 50.0 if proportion_nonzero > 0.9 else 500.0 X, y = _make_sparse_offset_regression( bias=10, n_features=30, proportion_nonzero=proportion_nonzero, noise=noise, random_state=global_random_seed, n_samples=n_samples, ) # Manually scale the data to avoid pathological cases. We use # minmax_scale to deal with the sparse case without breaking # the sparsity pattern. X = minmax_scale(X) svd_ridge = Ridge(solver="svd", alpha=alpha).fit(X, y) X = X.astype(dtype, copy=False) y = y.astype(dtype, copy=False) if sparse_container is not None: X = sparse_container(X) if solver == "ridgecv": ridge = RidgeCV(alphas=[alpha]) else: if solver.startswith("sag"): # Avoid ConvergenceWarning for sag and saga solvers. tol = 1e-7 max_iter = 100_000 else: tol = 1e-10 max_iter = None ridge = Ridge( alpha=alpha, solver=solver, max_iter=max_iter, tol=tol, random_state=global_random_seed, ) ridge.fit(X, y) > assert_allclose(ridge.coef_, svd_ridge.coef_, atol=1e-3, rtol=1e-3) sklearn/linear_model/tests/test_ridge.py:796: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([ 135.15573 , 13.862679 , 313.97693 , 132.889 , -91.00739 , -181.79582 , 193.19571 , 334.3...7256, -19.636393 , 235.94742 , -212.01407 , -142.38306 , -87.80179 , 128.91441 ], dtype=float32) desired = array([ 47.2473648 , 22.28196053, 188.02558629, 95.27163797, 35.62150556, -48.16112122, 105.14239266, 205.61...99291, 3.29735456, -1.09620493, 70.11423103, -74.65236352, -14.69814929, -12.76152384, 71.48126144]) rtol = 0.001, atol = 0.001, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.001, atol=0.001 E E Mismatched elements: 30 / 30 (100%) E Max absolute difference among violations: 237.38324474 E Max relative difference among violations: 16.91306721 E ACTUAL: array([ 135.15573 , 13.862679, 313.97693 , 132.889 , -91.00739 , E -181.79582 , 193.19571 , 334.39825 , -32.902164, -213.63416 , E -145.5477 , 118.61369 , 123.5661 , 170.56786 , -28.266537,... E DESIRED: array([ 47.247365, 22.281961, 188.025586, 95.271638, 35.621506, E -48.161121, 105.142393, 205.611635, 36.006209, -99.461958, E 39.617627, 86.874819, 101.547812, 145.163201, -8.282467,... sklearn/utils/_testing.py:233: AssertionError _________________________ test_dtype_match[sparse_cg] __________________________ solver = 'sparse_cg' @pytest.mark.parametrize( "solver", ["svd", "sparse_cg", "cholesky", "lsqr", "sag", "saga", "lbfgs"] ) def test_dtype_match(solver): rng = np.random.RandomState(0) alpha = 1.0 positive = solver == "lbfgs" n_samples, n_features = 6, 5 X_64 = rng.randn(n_samples, n_features) y_64 = rng.randn(n_samples) X_32 = X_64.astype(np.float32) y_32 = y_64.astype(np.float32) tol = 2 * np.finfo(np.float32).resolution # Check type consistency 32bits ridge_32 = Ridge( alpha=alpha, solver=solver, max_iter=500, tol=tol, positive=positive ) ridge_32.fit(X_32, y_32) coef_32 = ridge_32.coef_ # Check type consistency 64 bits ridge_64 = Ridge( alpha=alpha, solver=solver, max_iter=500, tol=tol, positive=positive ) ridge_64.fit(X_64, y_64) coef_64 = ridge_64.coef_ # Do the actual checks at once for easier debug assert coef_32.dtype == X_32.dtype assert coef_64.dtype == X_64.dtype assert ridge_32.predict(X_32).dtype == X_32.dtype assert ridge_64.predict(X_64).dtype == X_64.dtype > assert_allclose(ridge_32.coef_, ridge_64.coef_, rtol=1e-4, atol=5e-4) sklearn/linear_model/tests/test_ridge.py:1959: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([-2.1077418 , 0.3304965 , 1.1042498 , 2.596676 , -0.13365616], dtype=float32) desired = array([-0.03352293, -0.28830017, -0.15873461, 0.09433474, 0.37841805]) rtol = 0.0001, atol = 0.0005, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.0001, atol=0.0005 E E Mismatched elements: 5 / 5 (100%) E Max absolute difference among violations: 2.50234137 E Max relative difference among violations: 61.874638 E ACTUAL: array([-2.107742, 0.330496, 1.10425 , 2.596676, -0.133656], E dtype=float32) E DESIRED: array([-0.033523, -0.2883 , -0.158735, 0.094335, 0.378418]) sklearn/utils/_testing.py:233: AssertionError ____________________________ test_dtype_match[lsqr] ____________________________ solver = 'lsqr' @pytest.mark.parametrize( "solver", ["svd", "sparse_cg", "cholesky", "lsqr", "sag", "saga", "lbfgs"] ) def test_dtype_match(solver): rng = np.random.RandomState(0) alpha = 1.0 positive = solver == "lbfgs" n_samples, n_features = 6, 5 X_64 = rng.randn(n_samples, n_features) y_64 = rng.randn(n_samples) X_32 = X_64.astype(np.float32) y_32 = y_64.astype(np.float32) tol = 2 * np.finfo(np.float32).resolution # Check type consistency 32bits ridge_32 = Ridge( alpha=alpha, solver=solver, max_iter=500, tol=tol, positive=positive ) ridge_32.fit(X_32, y_32) coef_32 = ridge_32.coef_ # Check type consistency 64 bits ridge_64 = Ridge( alpha=alpha, solver=solver, max_iter=500, tol=tol, positive=positive ) ridge_64.fit(X_64, y_64) coef_64 = ridge_64.coef_ # Do the actual checks at once for easier debug assert coef_32.dtype == X_32.dtype assert coef_64.dtype == X_64.dtype assert ridge_32.predict(X_32).dtype == X_32.dtype assert ridge_64.predict(X_64).dtype == X_64.dtype > assert_allclose(ridge_32.coef_, ridge_64.coef_, rtol=1e-4, atol=5e-4) sklearn/linear_model/tests/test_ridge.py:1959: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([-0.07036758, -0.42478988, -0.23581429, 0.13027789, 0.5229078 ], dtype=float32) desired = array([-0.03352293, -0.28830017, -0.15873461, 0.09433474, 0.37841805]) rtol = 0.0001, atol = 0.0005, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.0001, atol=0.0005 E E Mismatched elements: 5 / 5 (100%) E Max absolute difference among violations: 0.14448974 E Max relative difference among violations: 1.09908832 E ACTUAL: array([-0.070368, -0.42479 , -0.235814, 0.130278, 0.522908], E dtype=float32) E DESIRED: array([-0.033523, -0.2883 , -0.158735, 0.094335, 0.378418]) sklearn/utils/_testing.py:233: AssertionError ________________ test_ridge_regression_dtype_stability[0-lsqr] _________________ solver = 'lsqr', seed = 0 @pytest.mark.parametrize( "solver", ["svd", "cholesky", "lsqr", "sparse_cg", "sag", "saga", "lbfgs"] ) @pytest.mark.parametrize("seed", range(1)) def test_ridge_regression_dtype_stability(solver, seed): random_state = np.random.RandomState(seed) n_samples, n_features = 6, 5 X = random_state.randn(n_samples, n_features) coef = random_state.randn(n_features) y = np.dot(X, coef) + 0.01 * random_state.randn(n_samples) alpha = 1.0 positive = solver == "lbfgs" results = dict() # XXX: Sparse CG seems to be far less numerically stable than the # others, maybe we should not enable float32 for this one. atol = 1e-3 if solver == "sparse_cg" else 1e-5 for current_dtype in (np.float32, np.float64): results[current_dtype] = ridge_regression( X.astype(current_dtype), y.astype(current_dtype), alpha=alpha, solver=solver, random_state=random_state, sample_weight=None, positive=positive, max_iter=500, tol=1e-10, return_n_iter=False, return_intercept=False, ) assert results[np.float32].dtype == np.float32 assert results[np.float64].dtype == np.float64 > assert_allclose(results[np.float32], results[np.float64], atol=atol) sklearn/linear_model/tests/test_ridge.py:2025: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([-0.56544924, 3.0763946 , -0.39052692, -1.2415375 , -1.3425361 ], dtype=float32) desired = array([-0.22848903, 0.1493022 , -0.11714981, -1.42511269, -0.87208568]) rtol = 0.0001, atol = 1e-05, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.0001, atol=1e-05 E E Mismatched elements: 5 / 5 (100%) E Max absolute difference among violations: 2.92709236 E Max relative difference among violations: 19.60515232 E ACTUAL: array([-0.565449, 3.076395, -0.390527, -1.241537, -1.342536], E dtype=float32) E DESIRED: array([-0.228489, 0.149302, -0.11715 , -1.425113, -0.872086]) sklearn/utils/_testing.py:233: AssertionError ______________ test_ridge_regression_dtype_stability[0-sparse_cg] ______________ solver = 'sparse_cg', seed = 0 @pytest.mark.parametrize( "solver", ["svd", "cholesky", "lsqr", "sparse_cg", "sag", "saga", "lbfgs"] ) @pytest.mark.parametrize("seed", range(1)) def test_ridge_regression_dtype_stability(solver, seed): random_state = np.random.RandomState(seed) n_samples, n_features = 6, 5 X = random_state.randn(n_samples, n_features) coef = random_state.randn(n_features) y = np.dot(X, coef) + 0.01 * random_state.randn(n_samples) alpha = 1.0 positive = solver == "lbfgs" results = dict() # XXX: Sparse CG seems to be far less numerically stable than the # others, maybe we should not enable float32 for this one. atol = 1e-3 if solver == "sparse_cg" else 1e-5 for current_dtype in (np.float32, np.float64): results[current_dtype] = ridge_regression( X.astype(current_dtype), y.astype(current_dtype), alpha=alpha, solver=solver, random_state=random_state, sample_weight=None, positive=positive, max_iter=500, tol=1e-10, return_n_iter=False, return_intercept=False, ) assert results[np.float32].dtype == np.float32 assert results[np.float64].dtype == np.float64 > assert_allclose(results[np.float32], results[np.float64], atol=atol) sklearn/linear_model/tests/test_ridge.py:2025: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ actual = array([ -7193.2837, 38728.793 , -18888.686 , 6389.298 , -6208.1123], dtype=float32) desired = array([-0.22848903, 0.1493022 , -0.11714981, -1.42511269, -0.87208568]) rtol = 0.0001, atol = 0.001, equal_nan = True, err_msg = '', verbose = True def assert_allclose( actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True ): """dtype-aware variant of numpy.testing.assert_allclose This variant introspects the least precise floating point dtype in the input argument and automatically sets the relative tolerance parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64 in scikit-learn). `atol` is always left to 0. by default. It should be adjusted manually to an assertion-specific value in case there are null values expected in `desired`. The aggregate tolerance is `atol + rtol * abs(desired)`. Parameters ---------- actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional, default=None Relative tolerance. If None, it is set based on the provided arrays' dtypes. atol : float, optional, default=0. Absolute tolerance. equal_nan : bool, optional, default=True If True, NaNs will compare equal. err_msg : str, optional, default='' The error message to be printed in case of failure. verbose : bool, optional, default=True If True, the conflicting values are appended to the error message. Raises ------ AssertionError If actual and desired are not equal up to specified precision. See Also -------- numpy.testing.assert_allclose Examples -------- >>> import numpy as np >>> from sklearn.utils._testing import assert_allclose >>> x = [1e-5, 1e-3, 1e-1] >>> y = np.arccos(np.cos(x)) >>> assert_allclose(x, y, rtol=1e-5, atol=0) >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32) >>> assert_allclose(a, 1e-5) """ dtypes = [] actual, desired = np.asanyarray(actual), np.asanyarray(desired) dtypes = [actual.dtype, desired.dtype] if rtol is None: rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes] rtol = max(rtols) > np_assert_allclose( actual, desired, rtol=rtol, atol=atol, equal_nan=equal_nan, err_msg=err_msg, verbose=verbose, ) E AssertionError: E Not equal to tolerance rtol=0.0001, atol=0.001 E E Mismatched elements: 5 / 5 (100%) E Max absolute difference among violations: 38728.64366655 E Max relative difference among violations: 259397.67697196 E ACTUAL: array([ -7193.2837, 38728.793 , -18888.686 , 6389.298 , -6208.1123], E dtype=float32) E DESIRED: array([-0.228489, 0.149302, -0.11715 , -1.425113, -0.872086]) sklearn/utils/_testing.py:233: AssertionError _____________ test_estimators[KernelPCA()-check_estimators_dtypes] _____________ estimator = KernelPCA() check = functools.partial(, 'KernelPCA') request = > @parametrize_with_checks( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators(estimator, check, request): # Common tests for estimator instances with ignore_warnings( category=(FutureWarning, ConvergenceWarning, UserWarning, LinAlgWarning) ): > check(estimator) sklearn/tests/test_common.py:122: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ sklearn/utils/_testing.py:141: in wrapper return fn(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^ sklearn/utils/estimator_checks.py:2281: in check_estimators_dtypes estimator.fit(X_train, y) sklearn/base.py:1336: in wrapper return fit_method(estimator, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/_kernel_pca.py:444: in fit self._fit_transform_in_place(K) sklearn/decomposition/_kernel_pca.py:368: in _fit_transform_in_place self.eigenvalues_ = _check_psd_eigenvalues( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ lambdas = array([-6.71141672e+00, -3.98478484e+00, -7.22224236e-01, -4.13062572e-01, -2.21332073e-01, -1.01605415e-01, -5...e+00, 6.36406612e+00, 1.19193659e+01, 1.38293219e+01, 1.79609299e+01, 2.49213371e+01], dtype=float32) enable_warnings = False def _check_psd_eigenvalues(lambdas, enable_warnings=False): """Check the eigenvalues of a positive semidefinite (PSD) matrix. Checks the provided array of PSD matrix eigenvalues for numerical or conditioning issues and returns a fixed validated version. This method should typically be used if the PSD matrix is user-provided (e.g. a Gram matrix) or computed using a user-provided dissimilarity metric (e.g. kernel function), or if the decomposition process uses approximation methods (randomized SVD, etc.). It checks for three things: - that there are no significant imaginary parts in eigenvalues (more than 1e-5 times the maximum real part). If this check fails, it raises a ``ValueError``. Otherwise all non-significant imaginary parts that may remain are set to zero. This operation is traced with a ``PositiveSpectrumWarning`` when ``enable_warnings=True``. - that eigenvalues are not all negative. If this check fails, it raises a ``ValueError`` - that there are no significant negative eigenvalues with absolute value more than 1e-10 (1e-6) and more than 1e-5 (5e-3) times the largest positive eigenvalue in double (simple) precision. If this check fails, it raises a ``ValueError``. Otherwise all negative eigenvalues that may remain are set to zero. This operation is traced with a ``PositiveSpectrumWarning`` when ``enable_warnings=True``. Finally, all the positive eigenvalues that are too small (with a value smaller than the maximum eigenvalue multiplied by 1e-12 (2e-7)) are set to zero. This operation is traced with a ``PositiveSpectrumWarning`` when ``enable_warnings=True``. Parameters ---------- lambdas : array-like of shape (n_eigenvalues,) Array of eigenvalues to check / fix. enable_warnings : bool, default=False When this is set to ``True``, a ``PositiveSpectrumWarning`` will be raised when there are imaginary parts, negative eigenvalues, or extremely small non-zero eigenvalues. Otherwise no warning will be raised. In both cases, imaginary parts, negative eigenvalues, and extremely small non-zero eigenvalues will be set to zero. Returns ------- lambdas_fixed : ndarray of shape (n_eigenvalues,) A fixed validated copy of the array of eigenvalues. Examples -------- >>> from sklearn.utils.validation import _check_psd_eigenvalues >>> _check_psd_eigenvalues([1, 2]) # nominal case array([1, 2]) >>> _check_psd_eigenvalues([5, 5j]) # significant imag part Traceback (most recent call last): ... ValueError: There are significant imaginary parts in eigenvalues (1 of the maximum real part). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. >>> _check_psd_eigenvalues([5, 5e-5j]) # insignificant imag part array([5., 0.]) >>> _check_psd_eigenvalues([-5, -1]) # all negative Traceback (most recent call last): ... ValueError: All eigenvalues are negative (maximum is -1). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. >>> _check_psd_eigenvalues([5, -1]) # significant negative Traceback (most recent call last): ... ValueError: There are significant negative eigenvalues (0.2 of the maximum positive). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. >>> _check_psd_eigenvalues([5, -5e-5]) # insignificant negative array([5., 0.]) >>> _check_psd_eigenvalues([5, 4e-12]) # bad conditioning (too small) array([5., 0.]) """ lambdas = np.array(lambdas) is_double_precision = lambdas.dtype == np.float64 # note: the minimum value available is # - single-precision: np.finfo('float32').eps = 1.2e-07 # - double-precision: np.finfo('float64').eps = 2.2e-16 # the various thresholds used for validation # we may wish to change the value according to precision. significant_imag_ratio = 1e-5 significant_neg_ratio = 1e-5 if is_double_precision else 5e-3 significant_neg_value = 1e-10 if is_double_precision else 1e-6 small_pos_ratio = 1e-12 if is_double_precision else 2e-7 # Check that there are no significant imaginary parts if not np.isreal(lambdas).all(): max_imag_abs = np.abs(np.imag(lambdas)).max() max_real_abs = np.abs(np.real(lambdas)).max() if max_imag_abs > significant_imag_ratio * max_real_abs: raise ValueError( "There are significant imaginary parts in eigenvalues (%g " "of the maximum real part). Either the matrix is not PSD, or " "there was an issue while computing the eigendecomposition " "of the matrix." % (max_imag_abs / max_real_abs) ) # warn about imaginary parts being removed if enable_warnings: warnings.warn( "There are imaginary parts in eigenvalues (%g " "of the maximum real part). Either the matrix is not" " PSD, or there was an issue while computing the " "eigendecomposition of the matrix. Only the real " "parts will be kept." % (max_imag_abs / max_real_abs), PositiveSpectrumWarning, ) # Remove all imaginary parts (even if zero) lambdas = np.real(lambdas) # Check that there are no significant negative eigenvalues max_eig = lambdas.max() if max_eig < 0: raise ValueError( "All eigenvalues are negative (maximum is %g). " "Either the matrix is not PSD, or there was an " "issue while computing the eigendecomposition of " "the matrix." % max_eig ) else: min_eig = lambdas.min() if ( min_eig < -significant_neg_ratio * max_eig and min_eig < -significant_neg_value ): > raise ValueError( "There are significant negative eigenvalues (%g" " of the maximum positive). Either the matrix is " "not PSD, or there was an issue while computing " "the eigendecomposition of the matrix." % (-min_eig / max_eig) E ValueError: There are significant negative eigenvalues (0.269304 of the maximum positive). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. sklearn/utils/validation.py:2044: ValueError ________ test_estimators[KernelPCA()-check_transformer_preserve_dtypes] ________ estimator = KernelPCA() check = functools.partial(, 'KernelPCA') request = > @parametrize_with_checks( list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks ) def test_estimators(estimator, check, request): # Common tests for estimator instances with ignore_warnings( category=(FutureWarning, ConvergenceWarning, UserWarning, LinAlgWarning) ): > check(estimator) sklearn/tests/test_common.py:122: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ sklearn/utils/estimator_checks.py:2306: in check_transformer_preserve_dtypes X_trans1 = transformer.fit_transform(X_cast, y) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/utils/_set_output.py:316: in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/_kernel_pca.py:476: in fit_transform self.fit(X, **params) sklearn/base.py:1336: in wrapper return fit_method(estimator, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ sklearn/decomposition/_kernel_pca.py:444: in fit self._fit_transform_in_place(K) sklearn/decomposition/_kernel_pca.py:368: in _fit_transform_in_place self.eigenvalues_ = _check_psd_eigenvalues( _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ lambdas = array([-1.9319584e+01, -3.1356449e+00, -2.5093555e+00, -1.7023087e+00, -8.8461113e-01, -5.7023048e-01, -4.23816... 9.6708858e-01, 1.0699120e+00, 2.4626904e+00, 2.6019154e+00, 1.8645788e+01, 9.0205940e+01], dtype=float32) enable_warnings = False def _check_psd_eigenvalues(lambdas, enable_warnings=False): """Check the eigenvalues of a positive semidefinite (PSD) matrix. Checks the provided array of PSD matrix eigenvalues for numerical or conditioning issues and returns a fixed validated version. This method should typically be used if the PSD matrix is user-provided (e.g. a Gram matrix) or computed using a user-provided dissimilarity metric (e.g. kernel function), or if the decomposition process uses approximation methods (randomized SVD, etc.). It checks for three things: - that there are no significant imaginary parts in eigenvalues (more than 1e-5 times the maximum real part). If this check fails, it raises a ``ValueError``. Otherwise all non-significant imaginary parts that may remain are set to zero. This operation is traced with a ``PositiveSpectrumWarning`` when ``enable_warnings=True``. - that eigenvalues are not all negative. If this check fails, it raises a ``ValueError`` - that there are no significant negative eigenvalues with absolute value more than 1e-10 (1e-6) and more than 1e-5 (5e-3) times the largest positive eigenvalue in double (simple) precision. If this check fails, it raises a ``ValueError``. Otherwise all negative eigenvalues that may remain are set to zero. This operation is traced with a ``PositiveSpectrumWarning`` when ``enable_warnings=True``. Finally, all the positive eigenvalues that are too small (with a value smaller than the maximum eigenvalue multiplied by 1e-12 (2e-7)) are set to zero. This operation is traced with a ``PositiveSpectrumWarning`` when ``enable_warnings=True``. Parameters ---------- lambdas : array-like of shape (n_eigenvalues,) Array of eigenvalues to check / fix. enable_warnings : bool, default=False When this is set to ``True``, a ``PositiveSpectrumWarning`` will be raised when there are imaginary parts, negative eigenvalues, or extremely small non-zero eigenvalues. Otherwise no warning will be raised. In both cases, imaginary parts, negative eigenvalues, and extremely small non-zero eigenvalues will be set to zero. Returns ------- lambdas_fixed : ndarray of shape (n_eigenvalues,) A fixed validated copy of the array of eigenvalues. Examples -------- >>> from sklearn.utils.validation import _check_psd_eigenvalues >>> _check_psd_eigenvalues([1, 2]) # nominal case array([1, 2]) >>> _check_psd_eigenvalues([5, 5j]) # significant imag part Traceback (most recent call last): ... ValueError: There are significant imaginary parts in eigenvalues (1 of the maximum real part). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. >>> _check_psd_eigenvalues([5, 5e-5j]) # insignificant imag part array([5., 0.]) >>> _check_psd_eigenvalues([-5, -1]) # all negative Traceback (most recent call last): ... ValueError: All eigenvalues are negative (maximum is -1). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. >>> _check_psd_eigenvalues([5, -1]) # significant negative Traceback (most recent call last): ... ValueError: There are significant negative eigenvalues (0.2 of the maximum positive). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. >>> _check_psd_eigenvalues([5, -5e-5]) # insignificant negative array([5., 0.]) >>> _check_psd_eigenvalues([5, 4e-12]) # bad conditioning (too small) array([5., 0.]) """ lambdas = np.array(lambdas) is_double_precision = lambdas.dtype == np.float64 # note: the minimum value available is # - single-precision: np.finfo('float32').eps = 1.2e-07 # - double-precision: np.finfo('float64').eps = 2.2e-16 # the various thresholds used for validation # we may wish to change the value according to precision. significant_imag_ratio = 1e-5 significant_neg_ratio = 1e-5 if is_double_precision else 5e-3 significant_neg_value = 1e-10 if is_double_precision else 1e-6 small_pos_ratio = 1e-12 if is_double_precision else 2e-7 # Check that there are no significant imaginary parts if not np.isreal(lambdas).all(): max_imag_abs = np.abs(np.imag(lambdas)).max() max_real_abs = np.abs(np.real(lambdas)).max() if max_imag_abs > significant_imag_ratio * max_real_abs: raise ValueError( "There are significant imaginary parts in eigenvalues (%g " "of the maximum real part). Either the matrix is not PSD, or " "there was an issue while computing the eigendecomposition " "of the matrix." % (max_imag_abs / max_real_abs) ) # warn about imaginary parts being removed if enable_warnings: warnings.warn( "There are imaginary parts in eigenvalues (%g " "of the maximum real part). Either the matrix is not" " PSD, or there was an issue while computing the " "eigendecomposition of the matrix. Only the real " "parts will be kept." % (max_imag_abs / max_real_abs), PositiveSpectrumWarning, ) # Remove all imaginary parts (even if zero) lambdas = np.real(lambdas) # Check that there are no significant negative eigenvalues max_eig = lambdas.max() if max_eig < 0: raise ValueError( "All eigenvalues are negative (maximum is %g). " "Either the matrix is not PSD, or there was an " "issue while computing the eigendecomposition of " "the matrix." % max_eig ) else: min_eig = lambdas.min() if ( min_eig < -significant_neg_ratio * max_eig and min_eig < -significant_neg_value ): > raise ValueError( "There are significant negative eigenvalues (%g" " of the maximum positive). Either the matrix is " "not PSD, or there was an issue while computing " "the eigendecomposition of the matrix." % (-min_eig / max_eig) E ValueError: There are significant negative eigenvalues (0.214172 of the maximum positive). Either the matrix is not PSD, or there was an issue while computing the eigendecomposition of the matrix. sklearn/utils/validation.py:2044: ValueError _______________ test_randomized_svd_low_rank_all_dtypes[float32] _______________ dtype = dtype('float32') @pytest.mark.parametrize("dtype", (np.int32, np.int64, np.float32, np.float64)) def test_randomized_svd_low_rank_all_dtypes(dtype): # Check that extmath.randomized_svd is consistent with linalg.svd n_samples = 100 n_features = 500 rank = 5 k = 10 decimal = 5 if dtype == np.float32 else 7 dtype = np.dtype(dtype) # generate a matrix X of approximate effective rank `rank` and no noise # component (very structured signal): X = make_low_rank_matrix( n_samples=n_samples, n_features=n_features, effective_rank=rank, tail_strength=0.0, random_state=0, ).astype(dtype, copy=False) assert X.shape == (n_samples, n_features) # compute the singular values of X using the slow exact method U, s, Vt = linalg.svd(X, full_matrices=False) # Convert the singular values to the specific dtype U = U.astype(dtype, copy=False) s = s.astype(dtype, copy=False) Vt = Vt.astype(dtype, copy=False) for normalizer in ["auto", "LU", "QR"]: # 'none' would not be stable # compute the singular values of X using the fast approximate method > Ua, sa, Va = randomized_svd( X, k, power_iteration_normalizer=normalizer, random_state=0 ) sklearn/utils/tests/test_extmath.py:138: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ sklearn/utils/_param_validation.py:218: in wrapper return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ sklearn/utils/extmath.py:543: in randomized_svd return _randomized_svd( sklearn/utils/extmath.py:611: in _randomized_svd Uhat, s, Vt = linalg.svd( /usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: in wrapper return f(*arrays, *other_args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib64/python3.14/site-packages/scipy/linalg/_decomp_svd.py:110: in svd a1 = _asarray_validated(a, check_finite=check_finite) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ /usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:455: in _asarray_validated a = toarray(a) ^^^^^^^^^^ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ a = array([[-0.07660285, 0.17750147, -0.0745592 , ..., -0.03172389, 0.20518021, 0.00212212], [ 0.0268822... nan, nan, nan, ..., nan, nan, nan]], shape=(20, 100), dtype=float32) dtype = None, order = None @set_module('numpy') def asarray_chkfinite(a, dtype=None, order=None): """Convert the input to an array, checking for NaNs or Infs. Parameters ---------- a : array_like Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays. Success requires no NaNs or Infs. dtype : data-type, optional By default, the data-type is inferred from the input data. order : {'C', 'F', 'A', 'K'}, optional Memory layout. 'A' and 'K' depend on the order of input array a. 'C' row-major (C-style), 'F' column-major (Fortran-style) memory representation. 'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise 'K' (keep) preserve input order Defaults to 'C'. Returns ------- out : ndarray Array interpretation of `a`. No copy is performed if the input is already an ndarray. If `a` is a subclass of ndarray, a base class ndarray is returned. Raises ------ ValueError Raises ValueError if `a` contains NaN (Not a Number) or Inf (Infinity). See Also -------- asarray : Create and array. asanyarray : Similar function which passes through subclasses. ascontiguousarray : Convert input to a contiguous array. asfortranarray : Convert input to an ndarray with column-major memory order. fromiter : Create an array from an iterator. fromfunction : Construct an array by executing a function on grid positions. Examples -------- >>> import numpy as np Convert a list into an array. If all elements are finite, then ``asarray_chkfinite`` is identical to ``asarray``. >>> a = [1, 2] >>> np.asarray_chkfinite(a, dtype=float) array([1., 2.]) Raises ValueError if array_like contains Nans or Infs. >>> a = [1, 2, np.inf] >>> try: ... np.asarray_chkfinite(a) ... except ValueError: ... print('ValueError') ... ValueError """ a = asarray(a, dtype=dtype, order=order) if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all(): > raise ValueError( "array must not contain infs or NaNs") E ValueError: array must not contain infs or NaNs /usr/lib64/python3.14/site-packages/numpy/lib/_function_base_impl.py:665: ValueError =============================== warnings summary =============================== sklearn/utils/_test_common/instance_generator.py:804 sklearn/utils/_test_common/instance_generator.py:804 sklearn/utils/_test_common/instance_generator.py:804 sklearn/utils/_test_common/instance_generator.py:804 sklearn/utils/_test_common/instance_generator.py:804 sklearn/utils/_test_common/instance_generator.py:804 sklearn/utils/_test_common/instance_generator.py:804 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/_test_common/instance_generator.py:804: SkipTestWarning: Can't instantiate estimator FrozenEstimator warnings.warn(msg, SkipTestWarning) sklearn/utils/deprecation.py:71: 6 warnings sklearn/linear_model/tests/test_common.py: 2 warnings sklearn/linear_model/tests/test_passive_aggressive.py: 2 warnings sklearn/tests/test_common.py: 1 warning /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/deprecation.py:71: FutureWarning: Class PassiveAggressiveClassifier is deprecated; this is deprecated in version 1.8 and will be removed in 1.10. Use `SGDClassifier(loss='hinge', penalty=None, learning_rate='pa1', eta0=1.0)` instead. warnings.warn(msg, category=FutureWarning) sklearn/utils/deprecation.py:71: 6 warnings sklearn/linear_model/tests/test_common.py: 2 warnings sklearn/linear_model/tests/test_passive_aggressive.py: 2 warnings sklearn/tests/test_common.py: 1 warning /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/deprecation.py:71: FutureWarning: Class PassiveAggressiveRegressor is deprecated; this is deprecated in version 1.8 and will be removed in 1.10. Use `SGDRegressor(loss='epsilon_insensitive', penalty=None, learning_rate='pa1', eta0 = 1.0)` instead. warnings.warn(msg, category=FutureWarning) sklearn/utils/estimator_checks.py:365 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:365: SkipTestWarning: Explicit SKIP via _skip_test tag for estimator ClassifierChain. warnings.warn( sklearn/utils/estimator_checks.py:357 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:357: SkipTestWarning: Can't test estimator CountVectorizer which requires input of type InputTags(one_d_array=False, two_d_array=False, three_d_array=False, sparse=False, categorical=False, string=True, dict=False, positive_only=False, allow_nan=False, pairwise=False) warnings.warn( sklearn/utils/estimator_checks.py:357 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:357: SkipTestWarning: Can't test estimator DictVectorizer which requires input of type InputTags(one_d_array=False, two_d_array=False, three_d_array=False, sparse=False, categorical=False, string=False, dict=True, positive_only=False, allow_nan=False, pairwise=False) warnings.warn( sklearn/utils/estimator_checks.py:357 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:357: SkipTestWarning: Can't test estimator FeatureHasher which requires input of type InputTags(one_d_array=False, two_d_array=False, three_d_array=False, sparse=False, categorical=False, string=False, dict=True, positive_only=False, allow_nan=False, pairwise=False) warnings.warn( sklearn/utils/estimator_checks.py:357 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:357: SkipTestWarning: Can't test estimator HashingVectorizer which requires input of type InputTags(one_d_array=False, two_d_array=False, three_d_array=False, sparse=False, categorical=False, string=True, dict=False, positive_only=False, allow_nan=False, pairwise=False) warnings.warn( sklearn/utils/estimator_checks.py:357 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:357: SkipTestWarning: Can't test estimator IsotonicRegression which requires input of type InputTags(one_d_array=True, two_d_array=False, three_d_array=False, sparse=False, categorical=False, string=False, dict=False, positive_only=False, allow_nan=False, pairwise=False) warnings.warn( sklearn/utils/estimator_checks.py:357 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:357: SkipTestWarning: Can't test estimator LabelBinarizer which requires input of type InputTags(one_d_array=False, two_d_array=False, three_d_array=False, sparse=False, categorical=False, string=False, dict=False, positive_only=False, allow_nan=False, pairwise=False) warnings.warn( sklearn/utils/estimator_checks.py:357 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:357: SkipTestWarning: Can't test estimator LabelEncoder which requires input of type InputTags(one_d_array=False, two_d_array=False, three_d_array=False, sparse=False, categorical=False, string=False, dict=False, positive_only=False, allow_nan=False, pairwise=False) warnings.warn( sklearn/utils/estimator_checks.py:357 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:357: SkipTestWarning: Can't test estimator MultiLabelBinarizer which requires input of type InputTags(one_d_array=False, two_d_array=False, three_d_array=False, sparse=False, categorical=False, string=False, dict=False, positive_only=False, allow_nan=False, pairwise=False) warnings.warn( sklearn/utils/estimator_checks.py:365 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:365: SkipTestWarning: Explicit SKIP via _skip_test tag for estimator MultiOutputClassifier. warnings.warn( sklearn/utils/estimator_checks.py:357 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:357: SkipTestWarning: Can't test estimator PatchExtractor which requires input of type InputTags(one_d_array=False, two_d_array=False, three_d_array=True, sparse=False, categorical=False, string=False, dict=False, positive_only=False, allow_nan=False, pairwise=False) warnings.warn( sklearn/utils/estimator_checks.py:357 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:357: SkipTestWarning: Can't test estimator TfidfVectorizer which requires input of type InputTags(one_d_array=False, two_d_array=False, three_d_array=False, sparse=False, categorical=False, string=True, dict=False, positive_only=False, allow_nan=False, pairwise=False) warnings.warn( sklearn/utils/_typedefs.pyx:21 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/_typedefs.pyx:21: PytestCollectionWarning: cannot collect 'testing_make_array_from_typed_val' because it is not a function. def testing_make_array_from_typed_val(testing_type_t val): sklearn/cluster/tests/test_affinity_propagation.py::test_affinity_propagation_equal_points /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/cluster/_affinity_propagation.py:50: UserWarning: All samples have mutually equal similarities. Returning arbitrary cluster center(s). warnings.warn( sklearn/cluster/tests/test_birch.py::test_birch_predict[float64-42] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/cluster/_birch.py:711: ConvergenceWarning: Number of subclusters found (3) by BIRCH is less than (4). Decrease the threshold. warnings.warn( sklearn/cluster/tests/test_dbscan.py: 10 warnings sklearn/cluster/tests/test_optics.py: 2 warnings sklearn/manifold/tests/test_isomap.py: 1 warning sklearn/neighbors/tests/test_neighbors.py: 122 warnings sklearn/utils/tests/test_estimator_checks.py: 21 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/neighbors/_base.py:241: EfficiencyWarning: Precomputed sparse input was not sorted by row values. Use the function sklearn.neighbors.sort_graph_by_row_values to sort the input by row values, with warn_when_not_sorted=False to remove this warning. warnings.warn( sklearn/cluster/tests/test_hdbscan.py::test_hdbscan_usable_inputs[kwargs0-X0] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/cluster/_hdbscan/hdbscan.py:270: UserWarning: The minimum spanning tree contains edge weights with value infinity. Potentially, you are missing too many distances in the initial distance matrix for the given neighborhood size. warn( sklearn/cluster/tests/test_hierarchical.py::test_affinity_passed_to_fix_connectivity /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/cluster/_agglomerative.py:594: UserWarning: the number of connected components of the connectivity matrix is 2 > 1. Completing it to avoid stopping the tree early. connectivity, n_connected_components = _fix_connectivity( sklearn/cluster/tests/test_spectral.py::test_precomputed_nearest_neighbors_filtering[42] sklearn/cluster/tests/test_spectral.py::test_precomputed_nearest_neighbors_filtering[42] sklearn/manifold/tests/test_spectral_embedding.py::test_precomputed_nearest_neighbors_filtering sklearn/manifold/tests/test_spectral_embedding.py::test_precomputed_nearest_neighbors_filtering /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/manifold/_spectral_embedding.py:324: UserWarning: Graph is not fully connected, spectral embedding may not work as expected. warnings.warn( sklearn/cluster/tests/test_spectral.py::test_spectral_clustering_np_matrix_raises /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/cluster/tests/test_spectral.py:315: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray. X = np.matrix([[0.0, 2.0], [2.0, 0.0]]) sklearn/covariance/tests/test_graphical_lasso.py: 20 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/covariance/_empirical_covariance.py:100: UserWarning: Only one sample available. You may want to reshape your data array warnings.warn( sklearn/covariance/tests/test_graphical_lasso.py::test_graphical_lasso_cv[42] /usr/lib64/python3.14/site-packages/numpy/_core/_methods.py:190: RuntimeWarning: invalid value encountered in subtract x = asanyarray(arr - arrmean) sklearn/covariance/tests/test_robust_covariance.py::test_mcd[42] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/covariance/_robust_covariance.py:188: RuntimeWarning: Determinant has increased; this should not happen: log(det) > log(previous_det) (-55.919119282067527 > -56.525833956855102). You may want to try with a higher value of support_fraction (current value: 1.000). warnings.warn( sklearn/covariance/tests/test_robust_covariance.py::test_mcd[42] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/covariance/_robust_covariance.py:188: RuntimeWarning: Determinant has increased; this should not happen: log(det) > log(previous_det) (-55.919119282067527 > -57.200053127529287). You may want to try with a higher value of support_fraction (current value: 1.000). warnings.warn( sklearn/covariance/tests/test_robust_covariance.py::test_mcd[42] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/covariance/_robust_covariance.py:188: RuntimeWarning: Determinant has increased; this should not happen: log(det) > log(previous_det) (-55.919119282067527 > -56.532223754953954). You may want to try with a higher value of support_fraction (current value: 1.000). warnings.warn( sklearn/covariance/tests/test_robust_covariance.py::test_mcd_issue3367[42] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/covariance/_robust_covariance.py:793: UserWarning: The covariance matrix associated to your dataset is not full rank warnings.warn( sklearn/datasets/tests/test_base.py::test_load_sample_images /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/datasets/tests/test_base.py:242: UserWarning: Could not load sample images, PIL is not available. warnings.warn("Could not load sample images, PIL is not available.") sklearn/datasets/tests/test_base.py::test_load_sample_image /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/datasets/tests/test_base.py:251: UserWarning: Could not load sample images, PIL is not available. warnings.warn("Could not load sample images, PIL is not available.") sklearn/datasets/tests/test_openml.py::test_dataset_with_openml_error[True] sklearn/datasets/tests/test_openml.py::test_dataset_with_openml_error[False] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/datasets/_openml.py:1035: UserWarning: Version 2 of dataset anneal is inactive, meaning that issues have been found in the dataset. Try using a newer version from this URL: https://www.openml.org/data/v1/download/1/anneal.arff warn( sklearn/datasets/tests/test_svmlight_format.py: 16 warnings /usr/lib64/python3.14/site-packages/scipy/sparse/_data.py:73: RuntimeWarning: invalid value encountered in cast self.data.astype(dtype, casting=casting, copy=True), sklearn/datasets/tests/test_svmlight_format.py: 12 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/datasets/tests/test_svmlight_format.py:287: RuntimeWarning: invalid value encountered in cast X_input = X.astype(dtype) sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_shapes /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.393e-05, tolerance: 1.246e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:753: ConvergenceWarning: Early stopping the lars path, as the residues are small and the current value of alpha is no longer well controlled. 16 iterations, alpha=8.552e-03, previous alpha=7.501e-03, with an active set of 7 regressors. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:753: ConvergenceWarning: Early stopping the lars path, as the residues are small and the current value of alpha is no longer well controlled. 11 iterations, alpha=1.672e-03, previous alpha=9.088e-04, with an active set of 8 regressors. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:723: ConvergenceWarning: Regressors in active set degenerate. Dropping a regressor, after 8 iterations, i.e. alpha=4.116e-03, with an active set of 8 regressors, and the smallest cholesky pivot element being 2.220e-16. Reduce max_iter or increase eps parameters. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:723: ConvergenceWarning: Regressors in active set degenerate. Dropping a regressor, after 8 iterations, i.e. alpha=2.117e-03, with an active set of 8 regressors, and the smallest cholesky pivot element being 2.220e-16. Reduce max_iter or increase eps parameters. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:753: ConvergenceWarning: Early stopping the lars path, as the residues are small and the current value of alpha is no longer well controlled. 9 iterations, alpha=4.116e-03, previous alpha=2.017e-03, with an active set of 8 regressors. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:753: ConvergenceWarning: Early stopping the lars path, as the residues are small and the current value of alpha is no longer well controlled. 18 iterations, alpha=1.680e-03, previous alpha=1.126e-03, with an active set of 9 regressors. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:753: ConvergenceWarning: Early stopping the lars path, as the residues are small and the current value of alpha is no longer well controlled. 14 iterations, alpha=4.799e-03, previous alpha=4.554e-03, with an active set of 7 regressors. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:753: ConvergenceWarning: Early stopping the lars path, as the residues are small and the current value of alpha is no longer well controlled. 13 iterations, alpha=1.573e-03, previous alpha=1.078e-03, with an active set of 8 regressors. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:723: ConvergenceWarning: Regressors in active set degenerate. Dropping a regressor, after 10 iterations, i.e. alpha=3.853e-03, with an active set of 8 regressors, and the smallest cholesky pivot element being 2.220e-16. Reduce max_iter or increase eps parameters. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:723: ConvergenceWarning: Regressors in active set degenerate. Dropping a regressor, after 10 iterations, i.e. alpha=2.467e-03, with an active set of 8 regressors, and the smallest cholesky pivot element being 2.220e-16. Reduce max_iter or increase eps parameters. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:753: ConvergenceWarning: Early stopping the lars path, as the residues are small and the current value of alpha is no longer well controlled. 11 iterations, alpha=3.641e-03, previous alpha=1.575e-03, with an active set of 8 regressors. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:753: ConvergenceWarning: Early stopping the lars path, as the residues are small and the current value of alpha is no longer well controlled. 15 iterations, alpha=1.206e-03, previous alpha=1.206e-03, with an active set of 8 regressors. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:753: ConvergenceWarning: Early stopping the lars path, as the residues are small and the current value of alpha is no longer well controlled. 13 iterations, alpha=1.002e-02, previous alpha=9.487e-03, with an active set of 8 regressors. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:723: ConvergenceWarning: Regressors in active set degenerate. Dropping a regressor, after 10 iterations, i.e. alpha=4.339e-03, with an active set of 8 regressors, and the smallest cholesky pivot element being 2.220e-16. Reduce max_iter or increase eps parameters. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_estimator_clone /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_least_angle.py:753: ConvergenceWarning: Early stopping the lars path, as the residues are small and the current value of alpha is no longer well controlled. 13 iterations, alpha=4.016e-03, previous alpha=7.038e-04, with an active set of 8 regressors. warnings.warn( sklearn/decomposition/tests/test_dict_learning.py: 11 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e-07, tolerance: 3.217e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_encode_dtype_match[float32-lasso_cd] sklearn/decomposition/tests/test_dict_learning.py::test_sparse_encode_numerical_consistency[lasso_cd] sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_dtype_match[float32-lasso_cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.172e-07, tolerance: 1.198e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_sparse_encode_dtype_match[float32-lasso_cd] sklearn/decomposition/tests/test_dict_learning.py::test_sparse_encode_numerical_consistency[lasso_cd] sklearn/decomposition/tests/test_dict_learning.py::test_sparse_coder_dtype_match[float32-lasso_cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.364e-07, tolerance: 8.058e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py: 14 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 1.462e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_cd-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-threshold-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-omp-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_dtype_match[float32-float32-cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.576e-06, tolerance: 8.058e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_cd-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-threshold-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-omp-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.537e-07, tolerance: 1.462e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_cd-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-threshold-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-omp-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 1.134e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_cd-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-threshold-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-omp-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.537e-07, tolerance: 1.246e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py: 19 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e-07, tolerance: 4.439e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_cd-lars] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_cd-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-threshold-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-omp-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e-07, tolerance: 1.462e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_cd-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-threshold-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-omp-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e-06, tolerance: 1.198e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py: 25 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e-07, tolerance: 4.853e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py: 14 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.153e-07, tolerance: 1.462e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py: 15 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.537e-07, tolerance: 1.134e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py: 13 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e-07, tolerance: 8.058e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py: 14 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e-07, tolerance: 1.246e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py: 15 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e-07, tolerance: 5.441e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_cd-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-threshold-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-omp-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e-07, tolerance: 8.058e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py: 21 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 1.246e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py: 28 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e-07, tolerance: 4.853e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py: 10 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 4.439e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py: 22 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e-07, tolerance: 1.134e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py: 14 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 8.058e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py: 14 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e-07, tolerance: 4.647e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[float32-float32-lasso_cd-cd] sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-lasso_cd-lars] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e-07, tolerance: 5.441e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[int32-float64-lasso_cd-lars] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[int32-float64-lasso_cd-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[int64-float64-lasso_cd-lars] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[int64-float64-lasso_cd-cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.902e-05, tolerance: 6.000e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[int32-float64-lasso_cd-lars] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[int32-float64-lasso_cd-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[int64-float64-lasso_cd-lars] sklearn/decomposition/tests/test_dict_learning.py::test_dictionary_learning_dtype_match[int64-float64-lasso_cd-cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.802e-04, tolerance: 5.000e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-lasso_lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-lasso_cd-cd] sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-threshold-cd] sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-omp-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.960e-07, tolerance: 1.134e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-lasso_lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-lasso_cd-cd] sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-threshold-cd] sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-omp-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.431e-06, tolerance: 1.462e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-lasso_lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-lasso_cd-lars] sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-lasso_cd-cd] sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-lars-cd] sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-threshold-cd] sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-omp-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e-07, tolerance: 1.198e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_minibatch_dictionary_learning_dtype_match[float32-float32-lasso_cd-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.153e-07, tolerance: 1.198e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e-06, tolerance: 1.134e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.788e-07, tolerance: 8.058e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.172e-07, tolerance: 5.441e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.153e-07, tolerance: 1.134e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.537e-07, tolerance: 1.198e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e-07, tolerance: 3.217e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.576e-07, tolerance: 4.647e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 1.198e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e-07, tolerance: 4.647e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.960e-07, tolerance: 8.058e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e-06, tolerance: 1.134e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_dtype_match[float32-float32-cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.153e-07, tolerance: 1.246e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.086e-07, tolerance: 3.217e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.960e-07, tolerance: 5.441e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.960e-08, tolerance: 4.439e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e-06, tolerance: 1.462e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 5.441e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 4.647e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e-06, tolerance: 1.246e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.431e-06, tolerance: 1.198e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.431e-06, tolerance: 1.246e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.537e-07, tolerance: 8.058e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_kernel_pca.py: 28 warnings sklearn/linear_model/tests/test_passive_aggressive.py: 2 warnings sklearn/tests/test_calibration.py: 5 warnings sklearn/tests/test_multioutput.py: 39 warnings sklearn/utils/tests/test_estimator_checks.py: 1 warning /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_stochastic_gradient.py:733: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. warnings.warn( sklearn/decomposition/tests/test_nmf.py: 30 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/decomposition/_nmf.py:1720: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence. warnings.warn( sklearn/decomposition/tests/test_nmf.py: 13 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/decomposition/_nmf.py:2306: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence. warnings.warn( sklearn/decomposition/tests/test_nmf.py::test_nmf_fit_close[NMF-solver1] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/decomposition/_nmf.py:1720: ConvergenceWarning: Maximum number of iterations 600 reached. Increase it to improve convergence. warnings.warn( sklearn/decomposition/tests/test_nmf.py: 24 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/decomposition/_nmf.py:1573: UserWarning: The multiplicative update ('mu') solver cannot update zeros present in the initialization, and so leads to poorer results when used jointly with init='nndsvd'. You may try init='nndsvda' or init='nndsvdar' instead. warnings.warn( sklearn/decomposition/tests/test_nmf.py::test_non_negative_factorization_consistency[0.0-1.0-cd-random] sklearn/decomposition/tests/test_nmf.py::test_non_negative_factorization_consistency[0.0-1.0-cd-random] sklearn/decomposition/tests/test_nmf.py::test_non_negative_factorization_consistency[0.0-1.0-cd-nndsvd] sklearn/decomposition/tests/test_nmf.py::test_non_negative_factorization_consistency[0.0-1.0-cd-nndsvd] sklearn/decomposition/tests/test_nmf.py::test_non_negative_factorization_consistency[1.0-0.0-cd-random] sklearn/decomposition/tests/test_nmf.py::test_non_negative_factorization_consistency[1.0-0.0-cd-random] sklearn/decomposition/tests/test_nmf.py::test_non_negative_factorization_consistency[1.0-0.0-cd-nndsvd] sklearn/decomposition/tests/test_nmf.py::test_non_negative_factorization_consistency[1.0-0.0-cd-nndsvd] sklearn/tests/test_common.py::test_transformers_get_feature_names_out[NMF(max_iter=500)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/decomposition/_nmf.py:1720: ConvergenceWarning: Maximum number of iterations 500 reached. Increase it to improve convergence. warnings.warn( sklearn/decomposition/tests/test_nmf.py::test_nmf_negative_beta_loss[csr_matrix] sklearn/decomposition/tests/test_nmf.py::test_nmf_negative_beta_loss[csr_array] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/decomposition/_nmf.py:170: RuntimeWarning: divide by zero encountered in power sum_WH_beta = np.sum(WH**beta) sklearn/decomposition/tests/test_online_lda.py::test_lda_empty_docs[csr_matrix] sklearn/decomposition/tests/test_online_lda.py::test_lda_empty_docs[csr_matrix] sklearn/decomposition/tests/test_online_lda.py::test_lda_empty_docs[csr_array] sklearn/decomposition/tests/test_online_lda.py::test_lda_empty_docs[csr_array] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/decomposition/_lda.py:921: RuntimeWarning: invalid value encountered in scalar divide perword_bound = bound / word_cnt sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e-06, tolerance: 2.111e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 9.191e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-MiniBatchSparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.537e-07, tolerance: 1.060e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-MiniBatchSparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 1.175e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 2.711e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-MiniBatchSparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 6.145e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e-07, tolerance: 9.191e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.537e-07, tolerance: 1.228e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.669e-06, tolerance: 2.111e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 1.138e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e-07, tolerance: 1.060e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-MiniBatchSparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.537e-07, tolerance: 1.184e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-MiniBatchSparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-MiniBatchSparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 1.060e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.431e-06, tolerance: 2.111e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-MiniBatchSparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.537e-07, tolerance: 2.111e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.153e-07, tolerance: 2.111e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-MiniBatchSparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 1.184e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e-07, tolerance: 2.111e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-MiniBatchSparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e-06, tolerance: 2.711e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-MiniBatchSparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.537e-07, tolerance: 1.138e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-MiniBatchSparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.768e-07, tolerance: 6.236e-08 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-MiniBatchSparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.192e-06, tolerance: 1.138e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_dtype_match[float32-float32-cd-MiniBatchSparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.153e-07, tolerance: 1.138e-07 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_numerical_consistency[42-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.490e-08, tolerance: 2.975e-09 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_numerical_consistency[42-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.451e-09, tolerance: 2.802e-09 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_numerical_consistency[42-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e-07, tolerance: 4.367e-09 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_numerical_consistency[42-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.980e-08, tolerance: 3.588e-09 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_numerical_consistency[42-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.980e-08, tolerance: 3.230e-09 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_numerical_consistency[42-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.960e-08, tolerance: 3.622e-09 model = cd_fast.enet_coordinate_descent_gram( sklearn/decomposition/tests/test_sparse_pca.py::test_sparse_pca_numerical_consistency[42-cd-SparsePCA] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.490e-08, tolerance: 4.367e-09 model = cd_fast.enet_coordinate_descent_gram( sklearn/ensemble/tests/test_bagging.py: 33 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:1131: RuntimeWarning: divide by zero encountered in log log_proba = np.log(self.predict_proba(X, **params)) sklearn/ensemble/tests/test_bagging.py::test_oob_score_classification sklearn/ensemble/tests/test_bagging.py::test_oob_score_classification sklearn/ensemble/tests/test_bagging.py::test_oob_score_removed_on_warm_start /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:933: RuntimeWarning: invalid value encountered in divide oob_decision_function = predictions / predictions.sum(axis=1)[:, np.newaxis] sklearn/ensemble/tests/test_bagging.py::test_oob_score_removed_on_warm_start /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/ensemble/_bagging.py:927: UserWarning: Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates. warn( sklearn/ensemble/tests/test_bagging.py: 6 warnings sklearn/tree/tests/test_tree.py: 73 warnings sklearn/utils/tests/test_response.py: 1 warning /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/tree/_classes.py:1092: RuntimeWarning: divide by zero encountered in log return np.log(proba) sklearn/ensemble/tests/test_common.py: 6 warnings sklearn/model_selection/tests/test_validation.py: 63 warnings sklearn/tests/test_multioutput.py: 1 warning sklearn/tests/test_pipeline.py: 5 warnings sklearn/utils/_repr_html/tests/test_estimator.py: 1 warning sklearn/utils/tests/test_estimator_checks.py: 2 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1): STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT Increase the number of iterations to improve the convergence (max_iter=100). You might also want to scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( sklearn/ensemble/tests/test_forest.py::test_class_weight_errors[ExtraTreesClassifier] sklearn/ensemble/tests/test_forest.py::test_class_weight_errors[ExtraTreesClassifier] sklearn/ensemble/tests/test_forest.py::test_class_weight_errors[RandomForestClassifier] sklearn/ensemble/tests/test_forest.py::test_class_weight_errors[RandomForestClassifier] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/ensemble/_forest.py:860: UserWarning: class_weight presets "balanced" or "balanced_subsample" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use "balanced" weights, use compute_class_weight ("balanced", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter. warn( sklearn/ensemble/tests/test_forest.py::test_mse_criterion_object_segfault_smoke_test[ExtraTreesRegressor] sklearn/ensemble/tests/test_forest.py::test_mse_criterion_object_segfault_smoke_test[RandomForestRegressor] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/base.py:1336: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel(). return fit_method(estimator, *args, **kwargs) sklearn/ensemble/tests/test_stacking.py: 30 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/neural_network/_multilayer_perceptron.py:785: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet. warnings.warn( sklearn/ensemble/tests/test_weight_boosting.py: 9 warnings sklearn/tests/test_common.py: 8 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/ensemble/_weight_boosting.py:566: RuntimeWarning: divide by zero encountered in log np.log(sample_weight) sklearn/ensemble/tests/test_weight_boosting.py::test_sample_weights_infinite /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/ensemble/_weight_boosting.py:565: RuntimeWarning: overflow encountered in exp sample_weight = np.exp( sklearn/feature_extraction/tests/test_text.py::test_vectorizer_pipeline_grid_selection /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_split.py:813: UserWarning: The least populated class in y has only 4 members, which is less than n_splits=5. warnings.warn( sklearn/feature_extraction/tests/test_text.py::test_callable_analyzer_error[filename-FileNotFoundError--CountVectorizer] sklearn/feature_extraction/tests/test_text.py::test_callable_analyzer_error[filename-FileNotFoundError--TfidfVectorizer] sklearn/feature_extraction/tests/test_text.py::test_callable_analyzer_error[filename-FileNotFoundError--HashingVectorizer] sklearn/utils/tests/test_testing.py::test_turn_warnings_into_errors[warning_info0] sklearn/utils/tests/test_testing.py::test_turn_warnings_into_errors[warning_info1] sklearn/utils/tests/test_testing.py::test_turn_warnings_into_errors[warning_info2] /usr/lib/python3.14/site-packages/_pytest/raises.py:624: PytestWarning: matching against an empty string will *always* pass. If you want to check for an empty message you need to pass '^$'. If you don't want to match you should pass `None` or leave out the parameter. super().__init__(match=match, check=check) sklearn/feature_extraction/tests/test_text.py::test_unused_parameters_warn[None-None--ngram_range3-\\w+--'preprocessor'-'analyzer'-is callable-CountVectorizer] sklearn/feature_extraction/tests/test_text.py::test_unused_parameters_warn[None-None--ngram_range3-\\w+--'preprocessor'-'analyzer'-is callable-HashingVectorizer] sklearn/feature_extraction/tests/test_text.py::test_unused_parameters_warn[None-None--ngram_range3-\\w+--'preprocessor'-'analyzer'-is callable-TfidfVectorizer] sklearn/feature_extraction/tests/test_text.py::test_unused_parameters_warn[None-None--ngram_range3-\\w+--'preprocessor'-'analyzer'-is callable-TfidfVectorizer] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/feature_extraction/text.py:556: UserWarning: The parameter 'token_pattern' will not be used since 'analyzer' != 'word' warnings.warn( sklearn/feature_selection/tests/test_feature_select.py::test_invalid_k sklearn/feature_selection/tests/test_feature_select.py::test_invalid_k /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/feature_selection/_univariate_selection.py:110: UserWarning: Features [0] are constant. warnings.warn("Features %s are constant." % constant_features_idx, UserWarning) sklearn/feature_selection/tests/test_feature_select.py::test_invalid_k sklearn/feature_selection/tests/test_feature_select.py::test_invalid_k sklearn/feature_selection/tests/test_feature_select.py::test_f_classif_constant_feature /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/feature_selection/_univariate_selection.py:111: RuntimeWarning: invalid value encountered in divide f = msb / msw sklearn/feature_selection/tests/test_from_model.py: 4 warnings sklearn/tests/test_common.py: 12 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/feature_selection/_base.py:122: UserWarning: No features were selected: either the data is too noisy or the selection test too strict. warnings.warn( sklearn/feature_selection/tests/test_rfe.py: 48 warnings sklearn/model_selection/tests/test_search.py: 30 warnings sklearn/svm/tests/test_svm.py: 16 warnings sklearn/tests/test_calibration.py: 16 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/svm/_base.py:1258: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations. warnings.warn( sklearn/feature_selection/tests/test_variance_threshold.py::test_variance_nan[None] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/feature_selection/_variance_threshold.py:114: RuntimeWarning: Degrees of freedom <= 0 for slice. self.variances_ = np.nanvar(X, axis=0) sklearn/feature_selection/tests/test_variance_threshold.py::test_variance_nan[None] sklearn/feature_selection/tests/test_variance_threshold.py::test_variance_nan[bsr_matrix] sklearn/feature_selection/tests/test_variance_threshold.py::test_variance_nan[bsr_array] sklearn/feature_selection/tests/test_variance_threshold.py::test_variance_nan[csc_matrix] sklearn/feature_selection/tests/test_variance_threshold.py::test_variance_nan[csc_array] sklearn/feature_selection/tests/test_variance_threshold.py::test_variance_nan[csr_matrix] sklearn/feature_selection/tests/test_variance_threshold.py::test_variance_nan[csr_array] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/feature_selection/_variance_threshold.py:122: RuntimeWarning: All-NaN slice encountered self.variances_ = np.nanmin(compare_arr, axis=0) sklearn/gaussian_process/tests/test_gpc.py: 11 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified upper bound 100.0. Increasing the bound and calling fit again may find a better value. warnings.warn( sklearn/gaussian_process/tests/test_gpc.py::test_random_starts[42] sklearn/gaussian_process/tests/test_gpr.py::test_constant_target[kernel3] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/kernels.py:440: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__constant_value is close to the specified lower bound 0.01. Decreasing the bound and calling fit again may find a better value. warnings.warn( sklearn/gaussian_process/tests/test_gpc.py::test_multi_class[kernel0] sklearn/gaussian_process/tests/test_gpc.py::test_multi_class_n_jobs[kernel0] sklearn/gaussian_process/tests/test_gpr.py::test_constant_target[kernel0] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value. warnings.warn( sklearn/gaussian_process/tests/test_gpr.py: 25 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/kernels.py:440: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__constant_value is close to the specified lower bound 1e-05. Decreasing the bound and calling fit again may find a better value. warnings.warn( sklearn/gaussian_process/tests/test_gpr.py: 14 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/kernels.py:440: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__length_scale is close to the specified lower bound 0.001. Decreasing the bound and calling fit again may find a better value. warnings.warn( sklearn/gaussian_process/tests/test_gpr.py::test_random_starts /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/kernels.py:440: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__length_scale is close to the specified lower bound 0.0001. Decreasing the bound and calling fit again may find a better value. warnings.warn( sklearn/gaussian_process/tests/test_gpr.py::test_random_starts /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/kernels.py:440: ConvergenceWarning: The optimal value found for dimension 1 of parameter k1__k2__length_scale is close to the specified lower bound 0.0001. Decreasing the bound and calling fit again may find a better value. warnings.warn( sklearn/gaussian_process/tests/test_gpr.py::test_bound_check_fixed_hyperparameter /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/kernels.py:440: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__length_scale is close to the specified lower bound 1e-05. Decreasing the bound and calling fit again may find a better value. warnings.warn( sklearn/gaussian_process/tests/test_gpr.py::test_constant_target[kernel2] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter length_scale is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value. warnings.warn( sklearn/gaussian_process/tests/test_gpr.py::test_constant_target[kernel3] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__length_scale is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value. warnings.warn( sklearn/gaussian_process/tests/test_gpr.py::test_constant_target[kernel4] sklearn/gaussian_process/tests/test_gpr.py::test_constant_target[kernel5] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/kernels.py:440: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__constant_value is close to the specified lower bound 0.01. Decreasing the bound and calling fit again may find a better value. warnings.warn( sklearn/gaussian_process/tests/test_gpr.py::test_constant_target[kernel4] sklearn/gaussian_process/tests/test_gpr.py::test_constant_target[kernel5] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__length_scale is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value. warnings.warn( sklearn/gaussian_process/tests/test_gpr.py::test_gpr_fit_error[params1-ValueError-requires that all bounds are finite] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/kernels.py:355: RuntimeWarning: invalid value encountered in log return np.log(np.vstack(bounds)) sklearn/gaussian_process/tests/test_gpr.py::test_gpr_predict_input_not_modified /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/kernels.py:450: ConvergenceWarning: The optimal value found for dimension 0 of parameter constant_value is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value. warnings.warn( sklearn/gaussian_process/tests/test_gpr.py::test_gpr_predict_input_not_modified /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/gaussian_process/_gpr.py:490: UserWarning: Predicted variances smaller than 0. Setting those variances to 0. warnings.warn( sklearn/impute/tests/test_common.py: 16 warnings sklearn/impute/tests/test_impute.py: 1 warning /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/impute/_base.py:641: UserWarning: Skipping features without any observed values: [3]. At least one non-missing value is needed for imputation with strategy='mean'. warnings.warn( sklearn/impute/tests/test_common.py: 4 warnings sklearn/impute/tests/test_impute.py: 14 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/impute/_base.py:641: UserWarning: Skipping features without any observed values: [0]. At least one non-missing value is needed for imputation with strategy='mean'. warnings.warn( sklearn/impute/tests/test_impute.py: 11 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/impute/_base.py:641: UserWarning: Skipping features without any observed values: [0]. At least one non-missing value is needed for imputation with strategy='median'. warnings.warn( sklearn/impute/tests/test_impute.py: 16 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/impute/_base.py:641: UserWarning: Skipping features without any observed values: [0]. At least one non-missing value is needed for imputation with strategy='most_frequent'. warnings.warn( sklearn/impute/tests/test_impute.py: 10 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/impute/_base.py:641: UserWarning: Skipping features without any observed values: [3]. At least one non-missing value is needed for imputation with strategy='constant'. warnings.warn( sklearn/impute/tests/test_impute.py: 32 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/impute/_iterative.py:867: ConvergenceWarning: [IterativeImputer] Early stopping criterion not reached. warnings.warn( sklearn/impute/tests/test_impute.py::test_iterative_imputer_all_missing sklearn/impute/tests/test_impute.py::test_iterative_imputer_all_missing /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/impute/_base.py:641: UserWarning: Skipping features without any observed values: [0 1 2]. At least one non-missing value is needed for imputation with strategy='mean'. warnings.warn( sklearn/impute/tests/test_impute.py::test_iterative_imputer_min_max_value_remove_empty /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/impute/_base.py:641: UserWarning: Skipping features without any observed values: [2]. At least one non-missing value is needed for imputation with strategy='mean'. warnings.warn( sklearn/impute/tests/test_impute.py::test_iterative_imputer_with_empty_features[X_test0-constant] sklearn/impute/tests/test_impute.py::test_iterative_imputer_with_empty_features[X_test0-constant] sklearn/impute/tests/test_impute.py::test_iterative_imputer_with_empty_features[X_test1-constant] sklearn/impute/tests/test_impute.py::test_iterative_imputer_with_empty_features[X_test1-constant] sklearn/impute/tests/test_impute.py::test_iterative_imputer_with_empty_features[X_test2-constant] sklearn/impute/tests/test_impute.py::test_iterative_imputer_with_empty_features[X_test2-constant] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/impute/_base.py:641: UserWarning: Skipping features without any observed values: [0]. At least one non-missing value is needed for imputation with strategy='constant'. warnings.warn( sklearn/impute/tests/test_knn.py::test_knn_imputer_with_simple_example[-1-0] sklearn/impute/tests/test_knn.py::test_knn_imputer_with_simple_example[nan-0] sklearn/impute/tests/test_knn.py::test_knn_imputer_distance_weighted_not_enough_neighbors[-1-0] sklearn/impute/tests/test_knn.py::test_knn_imputer_distance_weighted_not_enough_neighbors[-1-0] sklearn/impute/tests/test_knn.py::test_knn_imputer_distance_weighted_not_enough_neighbors[nan-0] sklearn/impute/tests/test_knn.py::test_knn_imputer_distance_weighted_not_enough_neighbors[nan-0] sklearn/metrics/tests/test_pairwise.py::test_pairwise_distances_chunked_reduce[float64] sklearn/metrics/tests/test_pairwise.py::test_pairwise_distances_chunked_reduce_none[float64] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/_chunking.py:172: UserWarning: Could not adhere to working_memory config. Currently 0MiB, 1MiB required. warnings.warn( sklearn/linear_model/tests/test_common.py: 1 warning sklearn/linear_model/tests/test_passive_aggressive.py: 12 warnings sklearn/tests/test_multioutput.py: 1 warning /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_stochastic_gradient.py:1612: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. warnings.warn( sklearn/linear_model/tests/test_common.py::test_balance_property[42-True-LogisticRegression] sklearn/linear_model/tests/test_logistic.py::test_lr_penalty_l1ratio_incompatible[l1-0.0] sklearn/linear_model/tests/test_logistic.py::test_lr_penalty_l1ratio_incompatible[l2-1.0] sklearn/linear_model/tests/test_ridge.py::test_ridge_sample_weight_consistency[42-sag-wide-None-False] sklearn/linear_model/tests/test_ridge.py::test_ridge_sample_weight_consistency[42-sag-wide-csr_matrix-False] sklearn/linear_model/tests/test_ridge.py::test_ridge_sample_weight_consistency[42-sag-wide-csr_array-False] sklearn/linear_model/tests/test_ridge.py::test_ridge_sample_weight_consistency[42-saga-wide-None-False] sklearn/linear_model/tests/test_ridge.py::test_ridge_sample_weight_consistency[42-saga-wide-csr_matrix-False] sklearn/linear_model/tests/test_ridge.py::test_ridge_sample_weight_consistency[42-saga-wide-csr_array-False] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge warnings.warn( sklearn/linear_model/tests/test_coordinate_descent.py::test_linear_model_cv_alphas[ElasticNetCV] sklearn/linear_model/tests/test_coordinate_descent.py::test_linear_model_cv_alphas[ElasticNetCV] sklearn/linear_model/tests/test_coordinate_descent.py::test_linear_model_cv_alphas[LassoCV] sklearn/linear_model/tests/test_coordinate_descent.py::test_linear_model_cv_alphas[LassoCV] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/base.py:1336: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator return fit_method(estimator, *args, **kwargs) sklearn/linear_model/tests/test_coordinate_descent.py: 20 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: UserWarning: Coordinate descent with l1_reg=0 may lead to unexpected results and is discouraged. model = cd_fast.enet_coordinate_descent_multi_task( sklearn/linear_model/tests/test_coordinate_descent.py::test_linear_model_cv_alphas[MultiTaskLassoCV] sklearn/linear_model/tests/test_coordinate_descent.py::test_linear_model_cv_alphas[MultiTaskLassoCV] sklearn/linear_model/tests/test_coordinate_descent.py::test_linear_model_cv_alphas[MultiTaskElasticNetCV] sklearn/linear_model/tests/test_coordinate_descent.py::test_linear_model_cv_alphas[MultiTaskElasticNetCV] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:2756: UserWarning: Coordinate descent with l1_reg=0 may lead to unexpected results and is discouraged. ) = cd_fast.enet_coordinate_descent_multi_task( sklearn/linear_model/tests/test_huber.py::test_huber_max_iter /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_huber.py:348: ConvergenceWarning: lbfgs failed to converge after 1 iteration(s) (status=1): STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT Increase the number of iterations to improve the convergence (max_iter=1). You might also want to scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter) sklearn/linear_model/tests/test_logistic.py: 22 warnings sklearn/tests/test_calibration.py: 6 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_logistic.py:1170: UserWarning: Setting penalty=None will ignore the C and l1_ratio parameters warnings.warn( sklearn/linear_model/tests/test_logistic.py::test_elastic_net_l1_l2_equivalence[42-l1-1-0.001] sklearn/linear_model/tests/test_logistic.py::test_elastic_net_l1_l2_equivalence[42-l1-1-0.1] sklearn/linear_model/tests/test_logistic.py::test_elastic_net_l1_l2_equivalence[42-l1-1-1] sklearn/linear_model/tests/test_logistic.py::test_elastic_net_l1_l2_equivalence[42-l1-1-10] sklearn/linear_model/tests/test_logistic.py::test_elastic_net_l1_l2_equivalence[42-l1-1-100] sklearn/linear_model/tests/test_logistic.py::test_elastic_net_l1_l2_equivalence[42-l1-1-1000] sklearn/linear_model/tests/test_logistic.py::test_elastic_net_l1_l2_equivalence[42-l1-1-1000000.0] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only. warnings.warn( sklearn/linear_model/tests/test_logistic.py::test_l1_ratio_non_elasticnet /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_logistic.py:1160: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.5. penalty is deprecated. Please use l1_ratio only. warnings.warn( sklearn/linear_model/tests/test_omp.py::test_unreachable_accuracy sklearn/linear_model/tests/test_omp.py::test_unreachable_accuracy sklearn/linear_model/tests/test_omp.py::test_omp_cv sklearn/linear_model/tests/test_omp.py::test_omp_cv sklearn/linear_model/tests/test_omp.py::test_omp_cv sklearn/linear_model/tests/test_omp.py::test_omp_cv /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_omp.py:445: RuntimeWarning: Orthogonal matching pursuit ended prematurely due to linear dependence in the dictionary. The requested precision might not have been met. out = _cholesky_omp( sklearn/linear_model/tests/test_quantile.py::test_quantile_equals_huber_for_low_epsilon[True] sklearn/linear_model/tests/test_quantile.py::test_quantile_equals_huber_for_low_epsilon[False] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_huber.py:348: ConvergenceWarning: lbfgs failed to converge after 100 iteration(s) (status=1): STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT Increase the number of iterations to improve the convergence (max_iter=100). You might also want to scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter) sklearn/linear_model/tests/test_ridge.py::test_ridge_regression_unpenalized[wide-42-True-cholesky] sklearn/linear_model/tests/test_ridge.py::test_ridge_regression_unpenalized_hstacked_X[wide-42-True-cholesky] sklearn/linear_model/tests/test_ridge.py::test_ridge_regression_unpenalized_vstacked_X[wide-42-True-cholesky] sklearn/linear_model/tests/test_ridge.py::test_ridge_regression_unpenalized_vstacked_X[wide-42-False-cholesky] sklearn/linear_model/tests/test_ridge.py::test_ridgecv_alphas_zero[RidgeClassifierCV-3] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_ridge.py:268: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead. warnings.warn( sklearn/linear_model/tests/test_ridge.py::test_solver_consistency[42-20-float32-0.1-sparse_cg-None] /usr/lib64/python3.14/site-packages/scipy/sparse/linalg/_isolve/iterative.py:416: RuntimeWarning: overflow encountered in dot alpha = rho_cur / dotprod(p, q) sklearn/linear_model/tests/test_ridge.py::test_solver_consistency[42-20-float32-0.1-sparse_cg-None] /usr/lib64/python3.14/site-packages/scipy/sparse/linalg/_isolve/iterative.py:416: RuntimeWarning: invalid value encountered in dot alpha = rho_cur / dotprod(p, q) sklearn/linear_model/tests/test_ridge.py::test_ridge_classifier_with_scoring[None-cv1-_accuracy_callable] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:883: UserWarning: The scoring make_scorer(_accuracy_callable, response_method='predict') does not support sample_weight, which may lead to statistically incorrect results when fitting GridSearchCV(cv=KFold(n_splits=5, random_state=None, shuffle=False), estimator=RidgeClassifier(), param_grid={'alpha': array([ 0.1, 1. , 10. ])}, scoring=make_scorer(_accuracy_callable, response_method='predict')) with sample_weight. warnings.warn( sklearn/linear_model/tests/test_ridge.py::test_ridge_classifier_with_scoring[csr_matrix-cv1-_accuracy_callable] sklearn/linear_model/tests/test_ridge.py::test_ridge_classifier_with_scoring[csr_array-cv1-_accuracy_callable] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:883: UserWarning: The scoring make_scorer(_accuracy_callable, response_method='predict') does not support sample_weight, which may lead to statistically incorrect results when fitting GridSearchCV(cv=KFold(n_splits=5, random_state=None, shuffle=False), estimator=RidgeClassifier(solver='sparse_cg'), param_grid={'alpha': array([ 0.1, 1. , 10. ])}, scoring=make_scorer(_accuracy_callable, response_method='predict')) with sample_weight. warnings.warn( sklearn/linear_model/tests/test_ridge.py::test_ridge_regression_custom_scoring[None-cv1] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:883: UserWarning: The scoring make_scorer(_dummy_score, response_method='predict') does not support sample_weight, which may lead to statistically incorrect results when fitting GridSearchCV(cv=KFold(n_splits=5, random_state=None, shuffle=False), estimator=RidgeClassifier(), param_grid={'alpha': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])}, scoring=make_scorer(_dummy_score, response_method='predict')) with sample_weight. warnings.warn( sklearn/linear_model/tests/test_ridge.py::test_ridge_regression_custom_scoring[csr_matrix-cv1] sklearn/linear_model/tests/test_ridge.py::test_ridge_regression_custom_scoring[csr_array-cv1] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:883: UserWarning: The scoring make_scorer(_dummy_score, response_method='predict') does not support sample_weight, which may lead to statistically incorrect results when fitting GridSearchCV(cv=KFold(n_splits=5, random_state=None, shuffle=False), estimator=RidgeClassifier(solver='sparse_cg'), param_grid={'alpha': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])}, scoring=make_scorer(_dummy_score, response_method='predict')) with sample_weight. warnings.warn( sklearn/linear_model/tests/test_ridge.py::test_ridgecv_alphas_zero[RidgeCV-3] /usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=5.74507e-17): result may not be accurate. return f(*arrays, *other_args, **kwargs) sklearn/linear_model/tests/test_ridge.py::test_ridgecv_alphas_zero[RidgeCV-3] /usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=5.49293e-17): result may not be accurate. return f(*arrays, *other_args, **kwargs) sklearn/linear_model/tests/test_ridge.py::test_ridgecv_alphas_zero[RidgeCV-3] /usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=3.15218e-17): result may not be accurate. return f(*arrays, *other_args, **kwargs) sklearn/linear_model/tests/test_ridge.py: 57 warnings sklearn/model_selection/tests/test_search.py: 10 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_regression.py:1288: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples. warnings.warn(msg, UndefinedMetricWarning) sklearn/linear_model/tests/test_ridge.py::test_ridgecv_alphas_zero[RidgeCV-3] sklearn/linear_model/tests/test_ridge.py::test_ridgecv_sample_weight sklearn/linear_model/tests/test_ridge.py::test_ridgecv_sample_weight sklearn/linear_model/tests/test_ridge.py::test_ridgecv_sample_weight sklearn/linear_model/tests/test_ridge.py::test_ridgecv_sample_weight /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:1137: UserWarning: One or more of the test scores are non-finite: [nan nan nan] warnings.warn( sklearn/linear_model/tests/test_ridge.py::test_ridgecv_alphas_zero[RidgeCV-3] /usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=4.53866e-18): result may not be accurate. return f(*arrays, *other_args, **kwargs) sklearn/linear_model/tests/test_ridge.py::test_ridgecv_alphas_zero[RidgeClassifierCV-3] sklearn/model_selection/tests/test_search.py::test_search_with_2d_array /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_split.py:813: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3. warnings.warn( sklearn/linear_model/tests/test_ridge.py::test_ridgecv_alphas_zero[RidgeClassifierCV-3] /usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=7.76968e-17): result may not be accurate. return f(*arrays, *other_args, **kwargs) sklearn/linear_model/tests/test_ridge.py::test_ridgecv_alphas_zero[RidgeClassifierCV-3] /usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=2.38404e-17): result may not be accurate. return f(*arrays, *other_args, **kwargs) sklearn/linear_model/tests/test_sgd.py::test_sgd_oneclass_convergence sklearn/linear_model/tests/test_sgd.py::test_sgd_oneclass_convergence sklearn/linear_model/tests/test_sgd.py::test_sgd_oneclass_convergence /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_stochastic_gradient.py:2545: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit. warnings.warn( sklearn/manifold/tests/test_isomap.py::test_transform[float64-2-None] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/manifold/_isomap.py:384: UserWarning: The number of connected components of the neighbors graph is 15 > 1. Completing the graph to fit Isomap might be slow. Increase the number of neighbors to avoid this issue. self._fit_transform(X) sklearn/manifold/tests/test_isomap.py: 133 warnings sklearn/tests/test_common.py: 18 warnings sklearn/utils/tests/test_graph.py: 4 warnings sklearn/utils/tests/test_validation.py: 16 warnings /usr/lib64/python3.14/site-packages/scipy/sparse/_index.py:168: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil and dok are more efficient. self._set_intXint(row, col, x.flat[0]) sklearn/manifold/tests/test_isomap.py::test_pipeline[float64-2-None] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/manifold/_isomap.py:384: UserWarning: The number of connected components of the neighbors graph is 7 > 1. Completing the graph to fit Isomap might be slow. Increase the number of neighbors to avoid this issue. self._fit_transform(X) sklearn/manifold/tests/test_isomap.py::test_get_feature_names_out /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/manifold/_isomap.py:384: UserWarning: The number of connected components of the neighbors graph is 3 > 1. Completing the graph to fit Isomap might be slow. Increase the number of neighbors to avoid this issue. self._fit_transform(X) sklearn/manifold/tests/test_mds.py::test_classical_mds_init_to_mds sklearn/manifold/tests/test_mds.py::test_classical_mds_init_to_mds /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/manifold/_mds.py:383: UserWarning: Explicit initial positions passed: performing only one init of the MDS instead of 4 warnings.warn( sklearn/manifold/tests/test_spectral_embedding.py::test_spectral_embedding_two_components[float32-lobpcg] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/manifold/_spectral_embedding.py:449: UserWarning: Exited at iteration 15 with accuracies [0.89169544 0.02349175 0.1955137 ] not reaching the requested tolerance 0.06905339658260345. Use iteration 1 instead with accuracy 0.039317186921834946. _, diffusion_map = lobpcg( sklearn/manifold/tests/test_spectral_embedding.py::test_spectral_embedding_two_components[float32-lobpcg] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/manifold/_spectral_embedding.py:449: UserWarning: Exited postprocessing with accuracies [2.47730043e-07 1.03124715e-01 1.48266414e-02] not reaching the requested tolerance 0.06905339658260345. _, diffusion_map = lobpcg( sklearn/metrics/tests/test_classification.py::test_classification_report_labels_subset_superset[labels2-False] sklearn/metrics/tests/test_classification.py::test_classification_report_labels_subset_superset[labels2-False] sklearn/metrics/tests/test_classification.py::test_classification_report_labels_subset_superset[labels2-False] sklearn/metrics/tests/test_common.py::test_averaging_multilabel_all_zeroes[precision_score] sklearn/metrics/tests/test_common.py::test_averaging_multilabel_all_zeroes[precision_score] sklearn/metrics/tests/test_common.py::test_averaging_multilabel_all_zeroes[precision_score] sklearn/metrics/tests/test_common.py::test_no_averaging_labels sklearn/metrics/tests/test_common.py::test_no_averaging_labels /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:1847: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) sklearn/metrics/tests/test_classification.py::test_classification_report_labels_subset_superset[labels2-False] sklearn/metrics/tests/test_classification.py::test_classification_report_labels_subset_superset[labels2-False] sklearn/metrics/tests/test_classification.py::test_classification_report_labels_subset_superset[labels2-False] sklearn/metrics/tests/test_common.py::test_averaging_multilabel_all_zeroes[recall_score] sklearn/metrics/tests/test_common.py::test_averaging_multilabel_all_zeroes[recall_score] sklearn/metrics/tests/test_common.py::test_averaging_multilabel_all_zeroes[recall_score] sklearn/metrics/tests/test_common.py::test_no_averaging_labels sklearn/metrics/tests/test_common.py::test_no_averaging_labels /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:1847: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) sklearn/metrics/tests/test_classification.py: 3 warnings sklearn/metrics/tests/test_common.py: 9 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:1847: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_warnings[params3-No samples of the positive class are present in `y_true`.] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:2453: RuntimeWarning: invalid value encountered in scalar divide positive_likelihood_ratio = pos_num / pos_denom sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_warnings[params3-No samples of the positive class are present in `y_true`.] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:2473: RuntimeWarning: invalid value encountered in scalar divide negative_likelihood_ratio = neg_num / neg_denom sklearn/metrics/tests/test_classification.py::test_likelihood_ratios sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_raise_warning_deprecation[True] sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_replace_undefined_by_worst sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_replace_undefined_by_0_fp[replace_undefined_by0-1.0] sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_replace_undefined_by_0_fp[replace_undefined_by1-inf] sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_replace_undefined_by_0_fp[replace_undefined_by2-2.0] sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_replace_undefined_by_0_fp[replace_undefined_by3-nan] sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_replace_undefined_by_0_fp[nan-nan] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/_param_validation.py:218: UndefinedMetricWarning: `positive_likelihood_ratio` is ill-defined and set to `np.nan`. Use the `replace_undefined_by` param to return func(*args, **kwargs) sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_replace_undefined_by_worst sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_replace_undefined_by_0_tn[replace_undefined_by0-1.0] sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_replace_undefined_by_0_tn[replace_undefined_by1-0.0] sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_replace_undefined_by_0_tn[replace_undefined_by2-0.5] sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_replace_undefined_by_0_tn[replace_undefined_by3-nan] sklearn/metrics/tests/test_classification.py::test_likelihood_ratios_replace_undefined_by_0_tn[nan-nan] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/_param_validation.py:218: UndefinedMetricWarning: `negative_likelihood_ratio` is ill-defined and set to `np.nan`. Use the `replace_undefined_by` param to control this behavior. To suppress this warning or turn it into an error, see Python's `warnings` module and `warnings.catch_warnings()`. return func(*args, **kwargs) sklearn/metrics/tests/test_classification.py::test_matthews_corrcoef[42] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:634: UserWarning: A single label was found in 'y_true' and 'y_pred'. For the confusion matrix to have the correct shape, use the 'labels' parameter to pass all known labels. warnings.warn( sklearn/metrics/tests/test_classification.py::test_brier_score_loss_invalid_inputs /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:207: UserWarning: Labels passed were ['eggs', 'spam', 'ham', 'yams']. But this function assumes labels are ordered lexicographically. Pass the ordered labels=['eggs', 'ham', 'spam', 'yams'] and ensure that the columns of y_prob correspond to this ordering. warnings.warn( sklearn/metrics/tests/test_classification.py::test_balanced_accuracy_score[y_true2-y_pred2] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:2938: UserWarning: y_pred contains classes not in y_true warnings.warn("y_pred contains classes not in y_true") sklearn/metrics/tests/test_classification.py::test_classification_metric_pos_label_types[classes3-precision_recall_fscore_support] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:1900: UserWarning: Note that pos_label (set to 'one') is ignored when average != 'binary' (got None). You may use labels=[pos_label] to specify a single positive class. warnings.warn( sklearn/metrics/tests/test_classification.py::test_d2_log_loss_score_label_order /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:207: UserWarning: Labels passed were [0, 2, 1]. But this function assumes labels are ordered lexicographically. Pass the ordered labels=[0, 1, 2] and ensure that the columns of y_prob correspond to this ordering. warnings.warn( sklearn/metrics/tests/test_classification.py::test_d2_brier_score_with_labels sklearn/metrics/tests/test_classification.py::test_d2_brier_score_with_labels /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:207: UserWarning: Labels passed were [2, 0, 1]. But this function assumes labels are ordered lexicographically. Pass the ordered labels=[0, 1, 2] and ensure that the columns of y_prob correspond to this ordering. warnings.warn( sklearn/metrics/tests/test_common.py: 301 warnings sklearn/tests/test_common.py: 28 warnings sklearn/utils/tests/test_estimator_checks.py: 2 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/externals/array_api_compat/numpy/_aliases.py:125: RuntimeWarning: invalid value encountered in cast return x.astype(dtype=dtype, copy=copy) sklearn/metrics/tests/test_common.py::test_normalize_option_binary_classification[top_k_accuracy_score] sklearn/metrics/tests/test_common.py::test_normalize_option_binary_classification[top_k_accuracy_score] sklearn/metrics/tests/test_common.py::test_returned_value_consistency[top_k_accuracy_score] sklearn/metrics/tests/test_ranking.py::test_top_k_accuracy_score_binary[y_score2-2-1] sklearn/metrics/tests/test_ranking.py::test_top_k_accuracy_score_binary[y_score5-2-1] sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[top_k_accuracy-top_k_accuracy_score] sklearn/metrics/tests/test_score_objects.py::test_classification_binary_scores[top_k_accuracy-top_k_accuracy_score] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_ranking.py:2153: UndefinedMetricWarning: 'k' (2) greater than or equal to 'n_classes' (2) will result in a perfect score and is therefore meaningless. warnings.warn( sklearn/metrics/tests/test_common.py: 75 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:1847: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) sklearn/metrics/tests/test_common.py::test_averaging_multilabel_all_zeroes[f0.5_score] sklearn/metrics/tests/test_common.py::test_averaging_multilabel_all_zeroes[f1_score] sklearn/metrics/tests/test_common.py::test_averaging_multilabel_all_zeroes[f2_score] sklearn/metrics/tests/test_common.py::test_multilabel_sample_weight_invariance[samples_f0.5_score] sklearn/metrics/tests/test_common.py::test_multilabel_sample_weight_invariance[samples_f1_score] sklearn/metrics/tests/test_common.py::test_multilabel_sample_weight_invariance[samples_f2_score] sklearn/metrics/tests/test_score_objects.py::test_classification_scorer_sample_weight sklearn/metrics/tests/test_score_objects.py::test_classification_scorer_sample_weight sklearn/metrics/tests/test_score_objects.py::test_classification_scorer_sample_weight /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:1847: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in samples with no true nor predicted labels. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) sklearn/metrics/tests/test_common.py::test_averaging_multilabel_all_zeroes[jaccard_score] sklearn/metrics/tests/test_common.py::test_averaging_multilabel_all_zeroes[jaccard_score] sklearn/metrics/tests/test_common.py::test_averaging_multilabel_all_zeroes[jaccard_score] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:1847: UndefinedMetricWarning: Jaccard is ill-defined and being set to 0.0 in labels with no true or predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) sklearn/metrics/tests/test_common.py: 25 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:1847: UndefinedMetricWarning: Jaccard is ill-defined and being set to 0.0 due to no true or predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) sklearn/metrics/tests/test_common.py: 2 warnings sklearn/metrics/tests/test_score_objects.py: 3 warnings sklearn/tests/test_multioutput.py: 6 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:1847: UndefinedMetricWarning: Jaccard is ill-defined and being set to 0.0 in samples with no true or predicted labels. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) sklearn/metrics/tests/test_common.py: 76 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:1847: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) sklearn/metrics/tests/test_common.py: 28 warnings sklearn/metrics/tests/test_score_objects.py: 4 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:1847: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) sklearn/metrics/tests/test_common.py: 25 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:1847: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) sklearn/metrics/tests/test_common.py::test_averaging_multilabel_all_zeroes[recall_score] sklearn/metrics/tests/test_common.py::test_multilabel_sample_weight_invariance[samples_recall_score] sklearn/metrics/tests/test_common.py::test_returned_value_consistency[samples_recall_score] sklearn/metrics/tests/test_score_objects.py::test_classification_scorer_sample_weight sklearn/metrics/tests/test_score_objects.py::test_classification_scorer_sample_weight sklearn/metrics/tests/test_score_objects.py::test_classification_scorer_sample_weight sklearn/metrics/tests/test_score_objects.py::test_classification_scorer_sample_weight /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_classification.py:1847: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, f"{metric.capitalize()} is", result.shape[0]) sklearn/metrics/tests/test_ranking.py::test_precision_recall_curve_drop_intermediate /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_ranking.py:1131: UserWarning: No positive class found in y_true, recall is set to one for all thresholds. warnings.warn( sklearn/metrics/tests/test_regression.py::test_multioutput_regression /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/tests/test_regression.py:174: RuntimeWarning: invalid value encountered in scalar divide - np.abs(y_true[:, i] - y_pred[:, i]).sum() sklearn/metrics/tests/test_regression.py::test_multioutput_regression sklearn/metrics/tests/test_regression.py::test_multioutput_regression sklearn/metrics/tests/test_regression.py::test_multioutput_regression sklearn/metrics/tests/test_regression.py::test_regression_metrics_at_limits sklearn/metrics/tests/test_regression.py::test_regression_metrics_at_limits sklearn/metrics/tests/test_regression.py::test_regression_multioutput_array sklearn/metrics/tests/test_regression.py::test_regression_multioutput_array sklearn/metrics/tests/test_regression.py::test_regression_multioutput_array sklearn/metrics/tests/test_regression.py::test_regression_multioutput_array /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_regression.py:949: RuntimeWarning: invalid value encountered in divide output_scores = 1 - (numerator / denominator) sklearn/metrics/tests/test_regression.py::test_multioutput_regression sklearn/metrics/tests/test_regression.py::test_regression_metrics_at_limits sklearn/metrics/tests/test_regression.py::test_regression_metrics_at_limits sklearn/metrics/tests/test_regression.py::test_regression_multioutput_array /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_regression.py:949: RuntimeWarning: divide by zero encountered in divide output_scores = 1 - (numerator / denominator) sklearn/mixture/tests/test_bayesian_mixture.py: 349 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/mixture/_bayesian_mixture.py:814: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.) log_wishart = self.n_components * np.float64( sklearn/mixture/tests/test_gaussian_mixture.py::test_regularisation sklearn/mixture/tests/test_gaussian_mixture.py::test_regularisation sklearn/mixture/tests/test_gaussian_mixture.py::test_regularisation sklearn/mixture/tests/test_gaussian_mixture.py::test_regularisation sklearn/mixture/tests/test_gaussian_mixture.py::test_regularisation sklearn/mixture/tests/test_gaussian_mixture.py::test_regularisation sklearn/mixture/tests/test_gaussian_mixture.py::test_regularisation sklearn/mixture/tests/test_gaussian_mixture.py::test_regularisation /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/base.py:1336: ConvergenceWarning: Number of distinct clusters (2) found smaller than n_clusters (10). Possibly due to duplicate points in X. return fit_method(estimator, *args, **kwargs) sklearn/mixture/tests/test_gaussian_mixture.py::test_gaussian_mixture_setting_best_params sklearn/mixture/tests/test_mixture.py::test_gaussian_mixture_n_iter[estimator0] sklearn/mixture/tests/test_mixture.py::test_gaussian_mixture_n_iter[estimator1] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/mixture/_base.py:293: ConvergenceWarning: Best performing initialization did not converge. Try different init parameters, or increase max_iter, tol, or check for degenerate data. warnings.warn( sklearn/model_selection/tests/test_search.py::test_SearchCV_with_fit_params[RandomizedSearchCV] sklearn/model_selection/tests/test_search.py::test_SearchCV_with_fit_params[RandomizedSearchCV] sklearn/model_selection/tests/test_search.py::test_SearchCV_with_fit_params[RandomizedSearchCV] sklearn/model_selection/tests/test_search.py::test_search_cv_sample_weight_equivalence[estimator1] sklearn/model_selection/tests/test_search.py::test_search_cv_sample_weight_equivalence[estimator1] sklearn/model_selection/tests/test_search.py::test_empty_cv_iterator_error sklearn/model_selection/tests/test_search.py::test_random_search_bad_cv /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:324: UserWarning: The total space of parameters 3 is smaller than n_iter=10. Running 3 iterations. For exhaustive searches, use GridSearchCV. warnings.warn( sklearn/model_selection/tests/test_search.py: 4 warnings sklearn/model_selection/tests/test_successive_halving.py: 1 warning sklearn/tests/test_metaestimators_metadata_routing.py: 8 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:324: UserWarning: The total space of parameters 2 is smaller than n_iter=10. Running 2 iterations. For exhaustive searches, use GridSearchCV. warnings.warn( sklearn/model_selection/tests/test_search.py::test_random_search_cv_results_multimetric sklearn/model_selection/tests/test_search.py::test_random_search_cv_results_multimetric sklearn/model_selection/tests/test_search.py::test_random_search_cv_results_multimetric sklearn/model_selection/tests/test_search.py::test_random_search_cv_results_multimetric sklearn/model_selection/tests/test_search.py::test_random_search_cv_results_multimetric sklearn/model_selection/tests/test_search.py::test_random_search_cv_results_multimetric /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:324: UserWarning: The total space of parameters 3 is smaller than n_iter=30. Running 3 iterations. For exhaustive searches, use GridSearchCV. warnings.warn( sklearn/model_selection/tests/test_search.py: 10 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py:927: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py", line 916, in _score scores = scorer(estimator, X_test, y_test, **score_params) TypeError: test_unsupported_sample_weight_scorer..fake_score_func() takes 2 positional arguments but 3 were given warnings.warn( sklearn/model_selection/tests/test_search.py::test_unsupported_sample_weight_scorer sklearn/model_selection/tests/test_search.py::test_search_cv_results_none_param /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:1137: UserWarning: One or more of the test scores are non-finite: [nan nan] warnings.warn( sklearn/model_selection/tests/test_search.py::test_grid_search_failing_classifier /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:1137: UserWarning: One or more of the test scores are non-finite: [0.5 0.5 nan] warnings.warn( sklearn/model_selection/tests/test_search.py: 1 warning sklearn/model_selection/tests/test_validation.py: 15 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_stochastic_gradient.py:1419: RuntimeWarning: divide by zero encountered in log return np.log(self.predict_proba(X)) sklearn/model_selection/tests/test_search.py::test_search_estimator_param[RandomizedSearchCV-param_distributions] sklearn/model_selection/tests/test_search.py::test_multi_metric_search_forwards_metadata[RandomizedSearchCV-param_distributions] sklearn/model_selection/tests/test_search.py::test_score_rejects_params_with_no_routing_enabled[RandomizedSearchCV-param_distributions] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:324: UserWarning: The total space of parameters 1 is smaller than n_iter=10. Running 1 iterations. For exhaustive searches, use GridSearchCV. warnings.warn( sklearn/model_selection/tests/test_split.py: 14 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_split.py:813: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=4. warnings.warn( sklearn/model_selection/tests/test_split.py: 14 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_split.py:813: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=6. warnings.warn( sklearn/model_selection/tests/test_split.py: 14 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_split.py:813: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=7. warnings.warn( sklearn/model_selection/tests/test_split.py: 14 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_split.py:1037: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=4. warnings.warn( sklearn/model_selection/tests/test_split.py: 14 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_split.py:1037: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=6. warnings.warn( sklearn/model_selection/tests/test_split.py: 14 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_split.py:1037: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=7. warnings.warn( sklearn/model_selection/tests/test_successive_halving.py::test_input_errors[params1-Cannot use parameter a as the resource since it is part of-HalvingRandomSearchCV] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:324: UserWarning: The total space of parameters 1 is smaller than n_iter=100. Running 1 iterations. For exhaustive searches, use GridSearchCV. warnings.warn( sklearn/model_selection/tests/test_successive_halving.py::test_groups_support[HalvingRandomSearchCV] sklearn/model_selection/tests/test_successive_halving.py::test_groups_support[HalvingRandomSearchCV] sklearn/model_selection/tests/test_successive_halving.py::test_groups_support[HalvingRandomSearchCV] sklearn/model_selection/tests/test_successive_halving.py::test_groups_support[HalvingRandomSearchCV] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:324: UserWarning: The total space of parameters 1 is smaller than n_iter=4. Running 1 iterations. For exhaustive searches, use GridSearchCV. warnings.warn( sklearn/model_selection/tests/test_successive_halving.py::test_groups_support[HalvingRandomSearchCV] sklearn/model_selection/tests/test_successive_halving.py::test_groups_support[HalvingRandomSearchCV] sklearn/model_selection/tests/test_successive_halving.py::test_groups_support[HalvingRandomSearchCV] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:324: UserWarning: The total space of parameters 1 is smaller than n_iter=2. Running 1 iterations. For exhaustive searches, use GridSearchCV. warnings.warn( sklearn/model_selection/tests/test_validation.py: 124 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_split.py:885: UserWarning: The groups parameter is ignored by StratifiedKFold warnings.warn( sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_decision_function_shape /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py:1352: RuntimeWarning: Number of classes in training fold (1) does not match total number of classes (2). Results may not be appropriate for your use case. To fix this, use a cross-validation technique resulting in properly stratified folds warnings.warn( sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_decision_function_shape sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_class_subset sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_class_subset /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py:1352: RuntimeWarning: Number of classes in training fold (7) does not match total number of classes (10). Results may not be appropriate for your use case. To fix this, use a cross-validation technique resulting in properly stratified folds warnings.warn( sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_unbalanced sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_unbalanced /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_split.py:813: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=2. warnings.warn( sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_unbalanced /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py:1352: RuntimeWarning: Number of classes in training fold (2) does not match total number of classes (3). Results may not be appropriate for your use case. To fix this, use a cross-validation technique resulting in properly stratified folds warnings.warn( sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_class_subset sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_class_subset sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_class_subset sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_class_subset sklearn/model_selection/tests/test_validation.py::test_cross_val_predict_class_subset /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py:1352: RuntimeWarning: Number of classes in training fold (8) does not match total number of classes (10). Results may not be appropriate for your use case. To fix this, use a cross-validation technique resulting in properly stratified folds warnings.warn( sklearn/model_selection/tests/test_validation.py::test_fit_and_score_working /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py:927: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py", line 916, in _score scores = scorer(estimator, X_test, y_test, **score_params) TypeError: 'dict' object is not callable warnings.warn( sklearn/neighbors/tests/test_lof.py::test_n_neighbors_attribute /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/neighbors/_lof.py:285: UserWarning: n_neighbors (500) is greater than the total number of samples (150). n_neighbors will be set to (n_samples - 1) for estimation. warnings.warn( sklearn/neighbors/tests/test_lof.py::test_hasattr_prediction sklearn/neighbors/tests/test_lof.py::test_hasattr_prediction /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/neighbors/_lof.py:285: UserWarning: n_neighbors (20) is greater than the total number of samples (3). n_neighbors will be set to (n_samples - 1) for estimation. warnings.warn( sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_n_features_in_after_fitting] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/neighbors/_lof.py:285: UserWarning: n_neighbors (20) is greater than the total number of samples (15). n_neighbors will be set to (n_samples - 1) for estimation. warnings.warn( sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_nan_inf] sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_estimators_nan_inf] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/neighbors/_lof.py:285: UserWarning: n_neighbors (20) is greater than the total number of samples (10). n_neighbors will be set to (n_samples - 1) for estimation. warnings.warn( sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_classifier_data_not_an_array] sklearn/neighbors/tests/test_lof.py::test_novelty_true_common_tests[LocalOutlierFactor(novelty=True)-check_classifier_data_not_an_array] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/neighbors/_lof.py:285: UserWarning: n_neighbors (20) is greater than the total number of samples (12). n_neighbors will be set to (n_samples - 1) for estimation. warnings.warn( sklearn/neighbors/tests/test_nca.py::test_callback /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/neighbors/_nca.py:337: ConvergenceWarning: [NeighborhoodComponentsAnalysis] NCA did not converge: STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT warn( sklearn/neighbors/tests/test_nearest_centroid.py::test_features_zero_var sklearn/neighbors/tests/test_nearest_centroid.py::test_error_zero_variances[array] sklearn/neighbors/tests/test_nearest_centroid.py::test_error_zero_variances[csr_matrix] sklearn/neighbors/tests/test_nearest_centroid.py::test_error_zero_variances[csr_array] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/neighbors/_nearest_centroid.py:241: UserWarning: self.within_class_std_dev_ has at least 1 zero standard deviation.Inputs within the same classes for at least 1 feature are identical. warnings.warn( sklearn/neighbors/tests/test_neighbors.py: 185 warnings sklearn/tests/test_common.py: 48 warnings /usr/lib64/python3.14/site-packages/numpy/_core/numeric.py:476: RuntimeWarning: invalid value encountered in cast multiarray.copyto(res, fill_value, casting='unsafe') sklearn/neighbors/tests/test_neighbors.py: 12 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/neighbors/_classification.py:878: RuntimeWarning: invalid value encountered in divide proba_k /= normalizer sklearn/neighbors/tests/test_neighbors.py::test_neighbors_validate_parameters[csr_matrix-KNeighborsClassifier] sklearn/neighbors/tests/test_neighbors.py::test_neighbors_validate_parameters[csr_matrix-RadiusNeighborsClassifier] sklearn/neighbors/tests/test_neighbors.py::test_neighbors_validate_parameters[csr_matrix-KNeighborsRegressor] sklearn/neighbors/tests/test_neighbors.py::test_neighbors_validate_parameters[csr_matrix-RadiusNeighborsRegressor] sklearn/neighbors/tests/test_neighbors.py::test_neighbors_validate_parameters[csr_array-KNeighborsClassifier] sklearn/neighbors/tests/test_neighbors.py::test_neighbors_validate_parameters[csr_array-RadiusNeighborsClassifier] sklearn/neighbors/tests/test_neighbors.py::test_neighbors_validate_parameters[csr_array-KNeighborsRegressor] sklearn/neighbors/tests/test_neighbors.py::test_neighbors_validate_parameters[csr_array-RadiusNeighborsRegressor] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/neighbors/_base.py:591: UserWarning: cannot use tree with sparse input: using brute force warnings.warn("cannot use tree with sparse input: using brute force") sklearn/neighbors/tests/test_neighbors.py: 11 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/neighbors/_unsupervised.py:179: SyntaxWarning: Parameter p is found in metric_params. The corresponding parameter from __init__ is ignored. return self._fit(X) sklearn/neighbors/tests/test_neighbors.py::test_valid_brute_metric_for_auto_algorithm[float64-csr_matrix-yule] sklearn/neighbors/tests/test_neighbors.py::test_valid_brute_metric_for_auto_algorithm[float64-csr_array-yule] sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[float64-42-yule] sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[float64-42-yule] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/pairwise.py:2459: DataConversionWarning: Data was converted to boolean for metric yule warnings.warn(msg, DataConversionWarning) sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_predict_proba sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_predict_proba /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/neighbors/_classification.py:868: UserWarning: Outlier label -1 is not in training classes. All class probabilities of outliers will be assigned with 0. warnings.warn( sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[float64-42-dice] sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[float64-42-dice] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/pairwise.py:2459: DataConversionWarning: Data was converted to boolean for metric dice warnings.warn(msg, DataConversionWarning) sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[float64-42-jaccard] sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[float64-42-jaccard] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/pairwise.py:2459: DataConversionWarning: Data was converted to boolean for metric jaccard warnings.warn(msg, DataConversionWarning) sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[float64-42-rogerstanimoto] sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[float64-42-rogerstanimoto] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/pairwise.py:2459: DataConversionWarning: Data was converted to boolean for metric rogerstanimoto warnings.warn(msg, DataConversionWarning) sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[float64-42-russellrao] sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[float64-42-russellrao] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/pairwise.py:2459: DataConversionWarning: Data was converted to boolean for metric russellrao warnings.warn(msg, DataConversionWarning) sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[float64-42-sokalmichener] sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[float64-42-sokalmichener] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/pairwise.py:2459: DataConversionWarning: Data was converted to boolean for metric sokalmichener warnings.warn(msg, DataConversionWarning) sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[float64-42-sokalsneath] sklearn/neighbors/tests/test_neighbors.py::test_radius_neighbors_brute_backend[float64-42-sokalsneath] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/pairwise.py:2459: DataConversionWarning: Data was converted to boolean for metric sokalsneath warnings.warn(msg, DataConversionWarning) sklearn/neighbors/tests/test_neighbors.py::test_nearest_neighbours_works_with_p_less_than_1 /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/neighbors/_base.py:661: UserWarning: Mind that for 0 < p < 1, Minkowski metrics are not distance metrics. Continuing the execution with `algorithm='brute'`. warnings.warn( sklearn/neighbors/tests/test_neighbors_pipeline.py::test_spectral_clustering sklearn/neighbors/tests/test_neighbors_pipeline.py::test_spectral_embedding /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/manifold/_spectral_embedding.py:305: UserWarning: Array is not symmetric, and will be converted to symmetric by average with its transpose. adjacency = check_symmetric(adjacency) sklearn/preprocessing/tests/test_common.py::test_missing_value_handling[est0-maxabs_scale-True-False-omit_kwargs0] sklearn/preprocessing/tests/test_common.py::test_missing_value_handling[est0-maxabs_scale-True-False-omit_kwargs0] sklearn/preprocessing/tests/test_common.py::test_missing_value_handling[est3-scale-True-False-omit_kwargs3] sklearn/preprocessing/tests/test_common.py::test_missing_value_handling[est3-scale-True-False-omit_kwargs3] sklearn/preprocessing/tests/test_common.py::test_missing_value_handling[est6-quantile_transform-True-False-omit_kwargs6] sklearn/preprocessing/tests/test_common.py::test_missing_value_handling[est6-quantile_transform-True-False-omit_kwargs6] sklearn/preprocessing/tests/test_common.py::test_missing_value_handling[est8-robust_scale-True-False-omit_kwargs8] sklearn/preprocessing/tests/test_common.py::test_missing_value_handling[est8-robust_scale-True-False-omit_kwargs8] /usr/lib64/python3.14/site-packages/scipy/sparse/_dia.py:76: SparseEfficiencyWarning: Constructing a DIA matrix with 115 diagonals is inefficient A = self._coo_container(arg1, dtype=dtype, shape=shape).todia() sklearn/preprocessing/tests/test_data.py::test_standard_scaler_sample_weight[array-Xw2-X2-sample_weight2] sklearn/preprocessing/tests/test_data.py::test_standard_scaler_sample_weight[array-Xw2-X2-sample_weight2] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/extmath.py:1207: RuntimeWarning: invalid value encountered in divide updated_mean = (last_sum + new_sum) / updated_sample_count sklearn/preprocessing/tests/test_data.py::test_standard_scaler_sample_weight[array-Xw2-X2-sample_weight2] sklearn/preprocessing/tests/test_data.py::test_standard_scaler_sample_weight[array-Xw2-X2-sample_weight2] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/extmath.py:1212: RuntimeWarning: invalid value encountered in divide T = new_sum / new_sample_count sklearn/preprocessing/tests/test_data.py::test_standard_scaler_sample_weight[array-Xw2-X2-sample_weight2] sklearn/preprocessing/tests/test_data.py::test_standard_scaler_sample_weight[array-Xw2-X2-sample_weight2] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/extmath.py:1236: RuntimeWarning: invalid value encountered in divide new_unnormalized_variance -= correction**2 / new_sample_count sklearn/preprocessing/tests/test_data.py::test_quantile_transform_and_inverse sklearn/preprocessing/tests/test_data.py::test_one_to_one_features[QuantileTransformer] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/preprocessing/_data.py:2885: UserWarning: n_quantiles (1000) is greater than the total number of samples (150). n_quantiles is set to n_samples. warnings.warn( sklearn/preprocessing/tests/test_data.py::test_quantile_transform_and_inverse /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/preprocessing/_data.py:2885: UserWarning: n_quantiles (1000) is greater than the total number of samples (7). n_quantiles is set to n_samples. warnings.warn( sklearn/preprocessing/tests/test_data.py::test_quantile_transform_nan /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/preprocessing/_data.py:2885: UserWarning: n_quantiles (10) is greater than the total number of samples (3). n_quantiles is set to n_samples. warnings.warn( sklearn/preprocessing/tests/test_data.py::test_quantile_transform_nan /usr/lib64/python3.14/site-packages/numpy/lib/_nanfunctions_impl.py:1617: RuntimeWarning: All-NaN slice encountered return fnb._ureduce(a, sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[quantile-averaged_inverted_cdf-expected5-sample_weight5] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/preprocessing/_discretization.py:396: UserWarning: Bins whose width are too small (i.e., <= 1e-8) in feature 1 are removed. Consider decreasing the number of bins. warnings.warn( sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[quantile-averaged_inverted_cdf-expected5-sample_weight5] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/preprocessing/_discretization.py:396: UserWarning: Bins whose width are too small (i.e., <= 1e-8) in feature 2 are removed. Consider decreasing the number of bins. warnings.warn( sklearn/preprocessing/tests/test_discretization.py::test_fit_transform_n_bins_array[quantile-averaged_inverted_cdf-expected5-sample_weight5] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/preprocessing/_discretization.py:396: UserWarning: Bins whose width are too small (i.e., <= 1e-8) in feature 3 are removed. Consider decreasing the number of bins. warnings.warn( sklearn/preprocessing/tests/test_discretization.py::test_redundant_bins[kmeans-expected_bin_edges1-warn] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/base.py:1336: ConvergenceWarning: Number of distinct clusters (2) found smaller than n_clusters (3). Possibly due to duplicate points in X. return fit_method(estimator, *args, **kwargs) sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscretizer_subsample_default sklearn/preprocessing/tests/test_discretization.py::test_kbinsdiscretizer_subsample_default /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/preprocessing/_discretization.py:396: UserWarning: Bins whose width are too small (i.e., <= 1e-8) in feature 0 are removed. Consider decreasing the number of bins. warnings.warn( sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_handle_unknown[warn] sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[None-False-warn] sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[None-True-warn] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/preprocessing/_encoders.py:248: UserWarning: Found unknown categories in columns [0, 1] during transform. These unknown categories will be encoded as all zeros warnings.warn( sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_handle_unknown_strings[warn] sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-warn] sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[numeric-warn] sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-string-warn] sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-string-none-warn] sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-string-nan-warn] sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_specified_categories[object-None-and-nan-warn] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/preprocessing/_encoders.py:248: UserWarning: Found unknown categories in columns [0] during transform. These unknown categories will be encoded as all zeros warnings.warn( sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[None-False-warn] sklearn/preprocessing/tests/test_encoders.py::test_one_hot_encoder_inverse[None-True-warn] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/preprocessing/_encoders.py:248: UserWarning: Found unknown categories in columns [1] during transform. These unknown categories will be encoded as all zeros warnings.warn( sklearn/svm/tests/test_svm.py::test_svc_nonfinite_params[42] /usr/lib64/python3.14/site-packages/numpy/_core/_methods.py:170: RuntimeWarning: overflow encountered in reduce arrmean = umr_sum(arr, axis, dtype, keepdims=True, where=where) sklearn/tests/test_calibration.py: 15 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/calibration.py:394: UserWarning: Since FrozenEstimator does not appear to accept sample_weight, sample weights will only be used for the calibration itself. This can be caused by a limitation of the current scikit-learn API. See the following issue for more details: https://github.com/scikit-learn/scikit-learn/issues/21134. Be warned that the result of the calibration is likely to be incorrect. warnings.warn( sklearn/tests/test_calibration.py::test_calibration_less_classes[False] sklearn/tests/test_calibration.py::test_calibration_less_classes[False] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py:1352: RuntimeWarning: Number of classes in training fold (3) does not match total number of classes (4). Results may not be appropriate for your use case. To fix this, use a cross-validation technique resulting in properly stratified folds warnings.warn( sklearn/tests/test_common.py::test_estimators[BayesianRidge(max_iter=5)-check_regressors_train(readonly_memmap=True,X_dtype=float32)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_bayes.py:366: RuntimeWarning: overflow encountered in dot self.sigma_ = np.dot( sklearn/tests/test_common.py::test_estimators[GaussianNB()-check_classifiers_one_label_sample_weights] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/naive_bayes.py:537: RuntimeWarning: divide by zero encountered in log jointi = xp.log(self.class_prior_[i]) sklearn/tests/test_common.py: 16 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/decomposition/_pca.py:584: RuntimeWarning: invalid value encountered in divide explained_variance_ = (S**2) / (n_samples - 1) sklearn/tests/test_common.py: 23 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/cluster/_bicluster.py:31: RuntimeWarning: divide by zero encountered in divide row_diag = np.asarray(1.0 / np.sqrt(X.sum(axis=1))).squeeze() sklearn/tests/test_common.py::test_estimators[SpectralCoclustering(n_clusters=2,n_init=2)-check_estimator_sparse_array] sklearn/tests/test_common.py::test_estimators[SpectralCoclustering(n_clusters=2,n_init=2)-check_estimator_sparse_matrix] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/cluster/_bicluster.py:346: RuntimeWarning: invalid value encountered in multiply z = np.vstack((row_diag[:, np.newaxis] * u, col_diag[:, np.newaxis] * v)) sklearn/tests/test_common.py::test_transformers_get_feature_names_out[FastICA(max_iter=5)] sklearn/tests/test_common.py::test_set_output_transform[FastICA(max_iter=5)] sklearn/tests/test_common.py::test_set_output_transform[FastICA(max_iter=5)] sklearn/tests/test_common.py::test_set_output_transform[FastICA(max_iter=5)] sklearn/tests/test_common.py::test_set_output_transform[FastICA(max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/decomposition/_fastica.py:132: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations. warnings.warn( sklearn/tests/test_common.py::test_transformers_get_feature_names_out[Isomap()] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/manifold/_isomap.py:384: UserWarning: The number of connected components of the neighbors graph is 2 > 1. Completing the graph to fit Isomap might be slow. Increase the number of neighbors to avoid this issue. self._fit_transform(X) sklearn/tests/test_common.py::test_transformers_get_feature_names_out[MiniBatchNMF(batch_size=10,fresh_restarts=True,max_iter=20)] sklearn/tests/test_common.py::test_set_output_transform[MiniBatchNMF(batch_size=10,fresh_restarts=True,max_iter=20)] sklearn/tests/test_common.py::test_set_output_transform[MiniBatchNMF(batch_size=10,fresh_restarts=True,max_iter=20)] sklearn/tests/test_common.py::test_set_output_transform[MiniBatchNMF(batch_size=10,fresh_restarts=True,max_iter=20)] sklearn/tests/test_common.py::test_set_output_transform[MiniBatchNMF(batch_size=10,fresh_restarts=True,max_iter=20)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/decomposition/_nmf.py:2306: ConvergenceWarning: Maximum number of iterations 20 reached. Increase it to improve convergence. warnings.warn( sklearn/tests/test_common.py::test_transformers_get_feature_names_out[Nystroem()] sklearn/tests/test_common.py::test_set_output_transform[Nystroem()] sklearn/tests/test_common.py::test_set_output_transform[Nystroem()] sklearn/tests/test_common.py::test_set_output_transform[Nystroem()] sklearn/tests/test_common.py::test_set_output_transform[Nystroem()] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/kernel_approximation.py:1024: UserWarning: n_components > n_samples. This is not possible. n_components was set to n_samples, which results in inefficient evaluation of the full kernel. warnings.warn( sklearn/tests/test_common.py::test_transformers_get_feature_names_out[QuantileTransformer()] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/preprocessing/_data.py:2885: UserWarning: n_quantiles (1000) is greater than the total number of samples (30). n_quantiles is set to n_samples. warnings.warn( sklearn/tests/test_common.py::test_set_output_transform[QuantileTransformer()] sklearn/tests/test_common.py::test_set_output_transform[QuantileTransformer()] sklearn/tests/test_common.py::test_set_output_transform[QuantileTransformer()] sklearn/tests/test_common.py::test_set_output_transform[QuantileTransformer()] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/preprocessing/_data.py:2885: UserWarning: n_quantiles (1000) is greater than the total number of samples (20). n_quantiles is set to n_samples. warnings.warn( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[AffinityPropagation(max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/cluster/_affinity_propagation.py:140: ConvergenceWarning: Affinity propagation did not converge, this model may return degenerate cluster centers and labels. warnings.warn( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.360e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.665e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.033e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.234e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.400e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.539e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.655e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.750e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.827e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.888e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.935e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.970e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.994e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.010e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.017e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.018e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.014e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.006e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.993e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.978e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.960e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.941e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.920e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.899e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.877e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.854e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.831e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.809e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.786e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.765e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.743e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.722e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.702e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.683e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.664e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.646e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.629e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.613e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.597e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.582e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.568e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.555e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.542e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.530e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.519e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.508e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.498e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.488e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.479e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.471e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.463e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.456e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.449e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.442e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.436e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.430e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.425e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.419e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.415e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.410e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.406e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.402e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.398e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.395e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.392e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.389e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.386e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.383e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.381e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.379e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.377e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.375e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.373e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.371e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.369e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.368e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.366e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.365e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.364e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.363e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.362e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.361e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.360e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.359e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.358e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.357e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[ElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.356e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.242e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.824e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.152e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.256e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.317e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.349e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.358e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.348e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.323e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.286e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.239e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.185e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.125e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.062e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.996e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.928e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.860e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.792e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.724e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.658e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.593e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.469e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.353e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.299e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.247e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.197e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.150e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.105e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.062e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.022e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.984e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.948e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.914e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.881e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.851e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.823e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.796e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.770e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.747e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.724e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.703e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.684e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.648e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.632e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.617e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.602e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.589e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.577e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.565e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.554e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.544e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.534e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.526e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.517e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.509e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.502e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.495e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.489e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.483e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.477e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.472e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.467e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.463e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.459e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.455e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.451e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.448e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.444e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.441e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.439e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.436e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.434e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.431e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.429e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.427e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.425e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.424e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.422e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.421e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.419e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.418e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.417e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.416e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[LassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.415e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_gram( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.360e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.665e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.033e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.234e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.400e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.539e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.655e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.750e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.827e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.888e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.935e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.970e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.994e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.010e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.017e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.018e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.014e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.006e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.993e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.978e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.960e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.941e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.920e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.899e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.877e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.854e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.831e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.809e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.786e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.765e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.743e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.722e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.702e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.683e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.664e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.646e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.629e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.613e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.597e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.582e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.568e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.555e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.542e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.530e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.519e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.508e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.498e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.488e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.479e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.471e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.463e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.456e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.449e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.442e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.436e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.430e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.425e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.419e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.415e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.410e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.406e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.402e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.398e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.395e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.392e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.389e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.386e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.383e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.381e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.379e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.377e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.375e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.373e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.371e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.369e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.368e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.366e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.365e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.364e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.363e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.362e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.361e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.360e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.359e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.358e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.357e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskElasticNetCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.356e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.242e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.824e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.152e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.256e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.317e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.349e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.358e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.348e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.323e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.286e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.239e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.185e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.125e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.062e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.996e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.928e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.860e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.792e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.724e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.658e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.593e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.469e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.353e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.299e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.247e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.197e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.150e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.105e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.062e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.022e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.984e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.948e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.914e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.881e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.851e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.823e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.796e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.770e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.747e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.724e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.703e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.684e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.648e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.632e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.617e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.602e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.589e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.577e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.565e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.554e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.544e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.534e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.526e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.517e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.509e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.502e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.495e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.489e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.483e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.477e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.472e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.467e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.463e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.459e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.455e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.451e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.448e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.444e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.441e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.439e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.436e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.434e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.431e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.429e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.427e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.425e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.424e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.422e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.421e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.419e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.418e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.417e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.416e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[MultiTaskLassoCV(cv=3,max_iter=5)] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:693: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.415e-02, tolerance: 6.864e-03 model = cd_fast.enet_coordinate_descent_multi_task( sklearn/tests/test_common.py::test_check_inplace_ensure_writeable[QuantileTransformer()] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/preprocessing/_data.py:2885: UserWarning: n_quantiles (1000) is greater than the total number of samples (100). n_quantiles is set to n_samples. warnings.warn( sklearn/tests/test_metaestimators.py: 1 warning sklearn/tests/test_metaestimators_metadata_routing.py: 30 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/semi_supervised/_self_training.py:244: UserWarning: y contains no unlabeled samples warnings.warn("y contains no unlabeled samples", UserWarning) sklearn/tests/test_metaestimators.py::test_meta_estimators_delegate_data_validation[HalvingRandomSearchCV0] sklearn/tests/test_metaestimators.py::test_meta_estimators_delegate_data_validation[HalvingRandomSearchCV2] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:324: UserWarning: The total space of parameters 2 is smaller than n_iter=3. Running 2 iterations. For exhaustive searches, use GridSearchCV. warnings.warn( sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[HalvingRandomSearchCV] sklearn/tests/test_metaestimators_metadata_routing.py::test_setting_request_on_sub_estimator_removes_error[HalvingRandomSearchCV] sklearn/tests/test_metaestimators_metadata_routing.py::test_non_consuming_estimator_works[HalvingRandomSearchCV] sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_is_routed_correctly_to_scorer[HalvingRandomSearchCV] sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_is_routed_correctly_to_scorer[HalvingRandomSearchCV] sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_is_routed_correctly_to_splitter[HalvingRandomSearchCV] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:324: UserWarning: The total space of parameters 2 is smaller than n_iter=8. Running 2 iterations. For exhaustive searches, use GridSearchCV. warnings.warn( sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_routed_to_group_splitter[HalvingRandomSearchCV] sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_routed_to_group_splitter[HalvingRandomSearchCV] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_search.py:324: UserWarning: The total space of parameters 2 is smaller than n_iter=4. Running 2 iterations. For exhaustive searches, use GridSearchCV. warnings.warn( sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_routed_to_group_splitter[GraphicalLassoCV] sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_routed_to_group_splitter[GraphicalLassoCV] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py:927: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py", line 916, in _score scores = scorer(estimator, X_test, y_test, **score_params) File "/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_scorer.py", line 317, in __call__ return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs) ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_scorer.py", line 408, in _score response_method = _check_response_method(estimator, self._response_method) File "/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/validation.py", line 2261, in _check_response_method raise AttributeError( ...<2 lines>... ) AttributeError: GraphicalLassoCV has none of the following attributes: predict. warnings.warn( sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_routed_to_group_splitter[SequentialFeatureSelector] sklearn/tests/test_metaestimators_metadata_routing.py::test_metadata_routed_to_group_splitter[SequentialFeatureSelector] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py:927: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: Traceback (most recent call last): File "/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/model_selection/_validation.py", line 916, in _score scores = scorer(estimator, X_test, y_test, **score_params) File "/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_scorer.py", line 317, in __call__ return self._score(partial(_cached_call, None), estimator, X, y_true, **_kwargs) ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/metrics/_scorer.py", line 408, in _score response_method = _check_response_method(estimator, self._response_method) File "/builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/validation.py", line 2261, in _check_response_method raise AttributeError( ...<2 lines>... ) AttributeError: SequentialFeatureSelector has none of the following attributes: predict. warnings.warn( sklearn/tests/test_multiclass.py::test_constant_int_target[ones] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 1 is present in all training examples. warnings.warn( sklearn/tests/test_multiclass.py::test_constant_int_target[zeros] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/multiclass.py:90: UserWarning: Label not 0 is present in all training examples. warnings.warn( sklearn/tests/test_naive_bayes.py::test_alpha_vector sklearn/tests/test_naive_bayes.py::test_alpha_vector /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/naive_bayes.py:655: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10. Use `force_alpha=True` to keep alpha unchanged. warnings.warn( sklearn/tree/tests/test_tree.py: 16 warnings /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/tree/_classes.py:1096: RuntimeWarning: divide by zero encountered in log proba[k] = np.log(proba[k]) sklearn/tree/tests/test_tree.py::test_big_input /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/_array_api.py:867: RuntimeWarning: overflow encountered in cast array = numpy.asarray(array, order=order, dtype=dtype) sklearn/tree/tests/test_tree.py::test_empty_leaf_infinite_threshold[None] sklearn/tree/tests/test_tree.py::test_empty_leaf_infinite_threshold[csc_matrix] sklearn/tree/tests/test_tree.py::test_empty_leaf_infinite_threshold[csc_array] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/tree/tests/test_tree.py:1891: RuntimeWarning: overflow encountered in cast data = xpx.nan_to_num(data.astype("float32")) sklearn/tree/tests/test_tree.py::test_empty_leaf_infinite_threshold[None] sklearn/tree/tests/test_tree.py::test_empty_leaf_infinite_threshold[None] sklearn/tree/tests/test_tree.py::test_empty_leaf_infinite_threshold[csc_matrix] sklearn/tree/tests/test_tree.py::test_empty_leaf_infinite_threshold[csc_array] /usr/lib64/python3.14/site-packages/numpy/_core/fromnumeric.py:86: RuntimeWarning: invalid value encountered in reduce return ufunc.reduce(obj, axis, dtype, out, **passkwargs) sklearn/utils/tests/test_estimator_checks.py::test_check_estimator sklearn/utils/tests/test_estimator_checks.py::test_check_estimator /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_sample_weights_pandas_series for LogisticRegression because it raised SkipTest: pandas is not installed: not testing for input of type pandas.Series to class weight. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator sklearn/utils/tests/test_estimator_checks.py::test_check_estimator /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_classifier_data_not_an_array for LogisticRegression because it raised SkipTest: pandas is not installed: not checking estimators for pandas objects. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_regressor_data_not_an_array for MultiTaskElasticNet because it raised SkipTest: pandas is not installed: not checking estimators for pandas objects. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_classifier_data_not_an_array for TaggedBinaryClassifier because it raised SkipTest: pandas is not installed: not checking estimators for pandas objects. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_regressor_data_not_an_array for RequiresPositiveXRegressor because it raised SkipTest: pandas is not installed: not checking estimators for pandas objects. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator sklearn/utils/tests/test_estimator_checks.py::test_check_estimator sklearn/utils/tests/test_extmath.py::test_randomized_svd_sparse_warnings[dok_matrix] sklearn/utils/tests/test_extmath.py::test_randomized_svd_sparse_warnings[dok_array] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/validation.py:981: UserWarning: Can't check dok sparse matrix for nan or inf. array = _ensure_sparse_format( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_regressor_data_not_an_array for RequiresPositiveYRegressor because it raised SkipTest: pandas is not installed: not checking estimators for pandas objects. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_sample_weights_pandas_series for PoorScoreLogisticRegression because it raised SkipTest: pandas is not installed: not testing for input of type pandas.Series to class weight. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_classifier_data_not_an_array for PoorScoreLogisticRegression because it raised SkipTest: pandas is not installed: not checking estimators for pandas objects. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_sample_weights_pandas_series for LinearRegression because it raised SkipTest: pandas is not installed: not testing for input of type pandas.Series to class weight. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_regressor_data_not_an_array for LinearRegression because it raised SkipTest: pandas is not installed: not checking estimators for pandas objects. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_sample_weights_pandas_series for SGDClassifier because it raised SkipTest: pandas is not installed: not testing for input of type pandas.Series to class weight. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_classifier_data_not_an_array for SGDClassifier because it raised SkipTest: pandas is not installed: not checking estimators for pandas objects. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_array_api_input for PCA because it raised SkipTest: SCIPY_ARRAY_API is not set: not checking array_api input warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_array_api_input for PCA because it raised SkipTest: array_api_strict is not installed: not checking array_api input warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_array_api_input for PCA because it raised SkipTest: cupy is not installed: not checking array_api input warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_array_api_input for PCA because it raised SkipTest: torch is not installed: not checking array_api input warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_clones /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_sample_weights_pandas_series for MiniBatchKMeans because it raised SkipTest: pandas is not installed: not testing for input of type pandas.Series to class weight. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_pairwise /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_sample_weights_pandas_series for SVC because it raised SkipTest: pandas is not installed: not testing for input of type pandas.Series to class weight. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_pairwise /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_classifier_data_not_an_array for SVC because it raised SkipTest: pandas is not installed: not checking estimators for pandas objects. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_check_estimator_pairwise /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:875: SkipTestWarning: Skipping check check_regressor_data_not_an_array for KNeighborsRegressor because it raised SkipTest: pandas is not installed: not checking estimators for pandas objects. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_non_deterministic_estimator_skip_tests /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:125: UserWarning: Estimator MinimalTransformer does not inherit from `sklearn.base.BaseEstimator`. This might lead to unexpected behavior, or even errors when collecting tests. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_non_deterministic_estimator_skip_tests /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:125: UserWarning: Estimator MyEstimator does not inherit from `sklearn.base.BaseEstimator`. This might lead to unexpected behavior, or even errors when collecting tests. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_non_deterministic_estimator_skip_tests /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:125: UserWarning: Estimator MinimalRegressor does not inherit from `sklearn.base.BaseEstimator`. This might lead to unexpected behavior, or even errors when collecting tests. warnings.warn( sklearn/utils/tests/test_estimator_checks.py::test_non_deterministic_estimator_skip_tests sklearn/utils/tests/test_estimator_checks.py::test_yield_all_checks_legacy /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/estimator_checks.py:125: UserWarning: Estimator MinimalClassifier does not inherit from `sklearn.base.BaseEstimator`. This might lead to unexpected behavior, or even errors when collecting tests. warnings.warn( sklearn/utils/tests/test_optimize.py::test_newton_cg_verbosity[2] sklearn/utils/tests/test_optimize.py::test_newton_cg_verbosity[2] /builddir/build/BUILD/python-scikit-learn-1.8.0_rc1-build/BUILDROOT/usr/lib64/python3.14/site-packages/sklearn/utils/optimize.py:312: UserWarning: Line Search failed warnings.warn("Line Search failed") -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html =========================== short test summary info ============================ FAILED sklearn/cluster/tests/test_spectral.py::test_cluster_qr[42] - assert F... FAILED sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_numerical_consistency[lars] FAILED sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_numerical_consistency[cd] FAILED sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[lars] FAILED sklearn/decomposition/tests/test_dict_learning.py::test_dict_learning_online_numerical_consistency[cd] FAILED sklearn/decomposition/tests/test_kernel_pca.py::test_32_64_decomposition_shape FAILED sklearn/decomposition/tests/test_nmf.py::test_nmf_dtype_match[NMF-solver0-float32-float32] FAILED sklearn/decomposition/tests/test_nmf.py::test_nmf_dtype_match[NMF-solver1-float32-float32] FAILED sklearn/decomposition/tests/test_nmf.py::test_nmf_dtype_match[MiniBatchNMF-solver2-float32-float32] FAILED sklearn/decomposition/tests/test_nmf.py::test_nmf_float32_float64_consistency[NMF-solver0] FAILED sklearn/decomposition/tests/test_nmf.py::test_nmf_float32_float64_consistency[NMF-solver1] FAILED sklearn/decomposition/tests/test_nmf.py::test_nmf_float32_float64_consistency[MiniBatchNMF-solver2] FAILED sklearn/decomposition/tests/test_online_lda.py::test_lda_numerical_consistency[42-batch] FAILED sklearn/decomposition/tests/test_online_lda.py::test_lda_numerical_consistency[42-online] FAILED sklearn/linear_model/tests/test_ridge.py::test_solver_consistency[42-20-float32-0.1-sparse_cg-None] FAILED sklearn/linear_model/tests/test_ridge.py::test_solver_consistency[42-20-float32-0.1-lsqr-None] FAILED sklearn/linear_model/tests/test_ridge.py::test_solver_consistency[42-40-float32-1.0-cholesky-None] FAILED sklearn/linear_model/tests/test_ridge.py::test_solver_consistency[42-40-float32-1.0-sparse_cg-None] FAILED sklearn/linear_model/tests/test_ridge.py::test_solver_consistency[42-40-float32-1.0-lsqr-None] FAILED sklearn/linear_model/tests/test_ridge.py::test_dtype_match[sparse_cg] FAILED sklearn/linear_model/tests/test_ridge.py::test_dtype_match[lsqr] - Ass... FAILED sklearn/linear_model/tests/test_ridge.py::test_ridge_regression_dtype_stability[0-lsqr] FAILED sklearn/linear_model/tests/test_ridge.py::test_ridge_regression_dtype_stability[0-sparse_cg] FAILED sklearn/tests/test_common.py::test_estimators[KernelPCA()-check_estimators_dtypes] FAILED sklearn/tests/test_common.py::test_estimators[KernelPCA()-check_transformer_preserve_dtypes] FAILED sklearn/utils/tests/test_extmath.py::test_randomized_svd_low_rank_all_dtypes[float32] = 26 failed, 33032 passed, 8496 skipped, 149 xfailed, 63 xpassed, 4045 warnings in 1940.34s (0:32:20) = error: Bad exit status from /var/tmp/rpm-tmp.1ISxI3 (%check) Bad exit status from /var/tmp/rpm-tmp.1ISxI3 (%check) RPM build errors: Finish: rpmbuild python-scikit-learn-1.8.0~rc1-1.fc44.src.rpm Finish: build phase for python-scikit-learn-1.8.0~rc1-1.fc44.src.rpm INFO: chroot_scan: 1 files copied to /var/lib/copr-rpmbuild/results/chroot_scan INFO: /var/lib/mock/fedora-43-ppc64le-1765205672.975907/root/var/log/dnf5.log INFO: chroot_scan: creating tarball /var/lib/copr-rpmbuild/results/chroot_scan.tar.gz /bin/tar: Removing leading `/' from member names ERROR: Exception(/var/lib/copr-rpmbuild/results/python-scikit-learn-1.8.0~rc1-1.fc44.src.rpm) Config(fedora-43-ppc64le) 51 minutes 51 seconds INFO: Results and/or logs in: /var/lib/copr-rpmbuild/results INFO: Cleaning up build root ('cleanup_on_failure=True') Start: clean chroot INFO: unmounting tmpfs. Finish: clean chroot ERROR: Command failed: # /usr/bin/systemd-nspawn -q -M 56ab6750a3ef46c693a64eba8624dc0d -D /var/lib/mock/fedora-43-ppc64le-1765205672.975907/root -a -u mockbuild --capability=cap_ipc_lock --capability=cap_ipc_lock --bind=/tmp/mock-resolv.2wvzer4l:/etc/resolv.conf --bind=/dev/btrfs-control --bind=/dev/mapper/control --bind=/dev/fuse --bind=/dev/loop-control --bind=/dev/loop0 --bind=/dev/loop1 --bind=/dev/loop2 --bind=/dev/loop3 --bind=/dev/loop4 --bind=/dev/loop5 --bind=/dev/loop6 --bind=/dev/loop7 --bind=/dev/loop8 --bind=/dev/loop9 --bind=/dev/loop10 --bind=/dev/loop11 --console=pipe --setenv=TERM=vt100 --setenv=SHELL=/bin/bash --setenv=HOME=/builddir --setenv=HOSTNAME=mock --setenv=PATH=/usr/bin:/bin:/usr/sbin:/sbin '--setenv=PROMPT_COMMAND=printf "\033]0;\007"' '--setenv=PS1= \s-\v\$ ' --setenv=LANG=C.UTF-8 --resolv-conf=off bash --login -c '/usr/bin/rpmbuild -ba --noprep --target ppc64le /builddir/build/originals/python-scikit-learn.spec' Copr build error: Build failed